Part2_Cryptocurrencies_tweet_sentiments.ipynb

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    "\n",
    "\n"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Big Data CS649 Project - Part2_Cryptocurrencies tweet sentiments analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Datasets :\n",
    "- Tweets dataset : https://www.kaggle.com/alaix14/bitcoin-tweets-20160101-to-20190329\n",
    "    - Contains all the bitcoin tweets\n",
    "    - date range is from 2016-Jan-01 to 2019-March-29"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running instruction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Packages that needs to be installed before running this notebook. Uncomment below lines once to install it and then comment it again.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\sk105659\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
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   "source": [
    "# ! pip install -U textblob\n",
    "# ! pip install google_trans_new\n",
    "\n",
    "# download english stopwords, this might take little time\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## configurations before running the notebook\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "######### Dataset input file path ###########################################################################\n",
    "# !curl -o tweets.csv https://www.kaggle.com/alaix14/bitcoin-tweets-20160101-to-20190329\n",
    "# Input dataset which need to downloaded from Kaggle as given above.\n",
    "\n",
    "bitcoin_tweets_dataset_filepath = './part2_input_dataset/tweets.csv'   \n",
    "  \n",
    "\n",
    "################## Number of records to be processed from the dataset input file ###########################\n",
    "\"\"\" This is important as the dataset is very huge and we have many bottleneck services like - \n",
    "1) google lan detector\n",
    "2) google lan translation, \n",
    "3) removing english verbose words from tweet text using stopwords from nltk.corpus\n",
    "4) finding sentiments out of english words using TextBlob \"\"\"\n",
    "\n",
    "no_of_records = 99999      # This seems to be a decent number as per our anlysis - output & performance \n",
    "# no_of_records = 1000    # To use small dataset for the devlopment purpose\n",
    "\n",
    "\n",
    "################## filter out dataframe over common date range for both both given datasets - price and tweets ##############\n",
    "start_date = pd.to_datetime(\"2017-03-01\").date()\n",
    "end_date = pd.to_datetime(\"2019-03-29\").date()\n",
    "    \n",
    "    \n",
    "######### flag to enable or disable english translation ##########################################################\n",
    "\n",
    "\"\"\" \n",
    "detecotor reached to limit after certain hits\n",
    "if get this error : google_new_transError: 429 (Too Many Requests) from TTS API. \n",
    "Then please change below flag from 0 to 1. \n",
    "Doing so, it will not convert non-english tweets to english lan and \n",
    "therefore the sentiments calucations would not be 100% accurate.\n",
    "\n",
    "Its a common issue for all free user : https://github.com/lushan88a/google_trans_new/issues/28\n",
    "\n",
    "we have tried many options like \n",
    "giving differnt proxies, making connection using private VPN, \n",
    "keep switching the VPN tunnels (specially to Mexico worked) to get the desired ouput for 99999 records.\n",
    "\n",
    "\"\"\"\n",
    "is_eng_translator_limit_reached = 0   # 0 if limit not recached, 1 if limit reached, \n",
    "is_detect_lang_required = 0           # 0 if not required , 1 if required. Turning this off is good as sometime it slows down or reach to thresold limit before the translation itself.\n",
    "\n",
    "\n",
    "\"\"\" Also note, once the translation is done, the output would be saved on the disk \n",
    "and so for all the future runs, no translation is required, ranslated data would be read from the saved disk. \"\"\"\n",
    "\n",
    "# Name of the file after google translation work is done once. \n",
    "processed_bitcoin_tweets_dataset_filepath = './part2_output/tweets_processed_eng_v1.csv'\n",
    "\n",
    "# This is the path of filename where notebook 1 has already processed bitcoin df and saved to disk\n",
    "# processed_bitcoin_price_filepath =  './processed_bitcoin.csv'\n",
    "processed_bitcoin_price_filepath =  './part1_output/processed_bitcoin.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from textblob import TextBlob\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.tseries.offsets import BDay\n",
    "\n",
    "\"\"\" function to read and clean the given dataset \"\"\"\n",
    "def read_and_clean_input_file(input_file, no_of_records=no_of_records):\n",
    "    \n",
    "    bitcoin_tweets_dataset_df_raw = pd.read_csv(\n",
    "        input_file, \n",
    "        sep=';',\n",
    "        nrows=no_of_records,\n",
    "        converters={'text': str},\n",
    "        usecols=['timestamp', 'text'],                            \n",
    "        index_col=[\"timestamp\"])\n",
    "    \n",
    "    ######### Strip time information from the column 'timestamp' if any  #########\n",
    "    #  bitcoin_tweets_dataset_df_raw['timestamp'] = pd.to_datetime(bitcoin_tweets_dataset_df_raw['timestamp']).dt.date\n",
    "    bitcoin_tweets_dataset_df_raw.index = pd.Series(pd.to_datetime(bitcoin_tweets_dataset_df_raw.index)).dt.date\n",
    "    return bitcoin_tweets_dataset_df_raw\n",
    "\n",
    "\n",
    "\"\"\" function to filter out df over common date range for both both given datasets - price and tweets\"\"\"\n",
    "def filter_by_daterange(df, start_date=start_date, end_date=end_date):\n",
    "    filtered_date_range = (df.index >= start_date) & (df.index <= end_date)\n",
    "    df = df[filtered_date_range]\n",
    "    ### Filter only Business day\n",
    "    is_business_day = BDay().is_on_offset\n",
    "    filtered_business_days = pd.to_datetime(df.index).map(is_business_day)\n",
    "    df[filtered_business_days]\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
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       "      <th>text</th>\n",
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       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-27</th>\n",
       "      <td>È appena uscito un nuovo video! LES CRYPTOMONN...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27</th>\n",
       "      <td>Cardano: Digitize Currencies; EOS https://t.co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27</th>\n",
       "      <td>Another Test tweet that wasn't caught in the s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27</th>\n",
       "      <td>Current Crypto Prices! \\n\\nBTC: $8721.99 USD\\n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27</th>\n",
       "      <td>Spiv (Nosar Baz): BITCOIN Is An Asset &amp;amp; NO...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27</th>\n",
       "      <td>#btc inceldiği yerden kopsun bakalım 17:00 ye ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27</th>\n",
       "      <td>@nwoodfine We have been building on the real #...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27</th>\n",
       "      <td>@pedronauck como investidor, vc é um ótimo dev...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27</th>\n",
       "      <td>ブラジルはまぁ置いといてもドイツは存在感出してくるのかな。ロシアもマイニングなどで元気になる...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27</th>\n",
       "      <td>CHANGE IS COMING...GET READY!!! Boom, Another ...</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "                                                         text\n",
       "timestamp                                                    \n",
       "2019-05-27  È appena uscito un nuovo video! LES CRYPTOMONN...\n",
       "2019-05-27  Cardano: Digitize Currencies; EOS https://t.co...\n",
       "2019-05-27  Another Test tweet that wasn't caught in the s...\n",
       "2019-05-27  Current Crypto Prices! \\n\\nBTC: $8721.99 USD\\n...\n",
       "2019-05-27  Spiv (Nosar Baz): BITCOIN Is An Asset &amp; NO...\n",
       "2019-05-27  #btc inceldiği yerden kopsun bakalım 17:00 ye ...\n",
       "2019-05-27  @nwoodfine We have been building on the real #...\n",
       "2019-05-27  @pedronauck como investidor, vc é um ótimo dev...\n",
       "2019-05-27  ブラジルはまぁ置いといてもドイツは存在感出してくるのかな。ロシアもマイニングなどで元気になる...\n",
       "2019-05-27  CHANGE IS COMING...GET READY!!! Boom, Another ..."
      ]
     },
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     "text": [
      "shape of dataset is  (10, 1)\n",
      "min date is  2019-05-27\n",
      "max date is  2019-05-27\n",
      "\n",
      " After filtering out on given date range \n"
     ]
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     "text": [
      "shape of dataset is  (0, 1)\n",
      "min date is  nan\n",
      "max date is  nan\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\"\"\" Below function is just for anlysing the datasets \"\"\"\n",
    "def analysing_dataset(df):\n",
    "    display(df)\n",
    "    #  display(df.info())\n",
    "    print('shape of dataset is ', df.shape)\n",
    "    print('min date is ', df.index.min())\n",
    "    print('max date is ', df.index.max())\n",
    "\n",
    "########### Testing block #########################################################################################\n",
    "temp_df = read_and_clean_input_file(bitcoin_tweets_dataset_filepath, no_of_records=10)\n",
    "analysing_dataset(temp_df)\n",
    "temp_df = filter_by_daterange(temp_df)\n",
    "print('\\n After filtering out on given date range ')\n",
    "analysing_dataset(temp_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['it', 'italian']\n",
      "A new video has just come out! Les Cryptomonn ... \n"
     ]
    }
   ],
   "source": [
    "from google_trans_new import google_translator  \n",
    "\n",
    "\n",
    "\"\"\" Below function is dedect and translate the non-eng tweets to eng tweets \"\"\"\n",
    "# translator = google_translator()\n",
    "# translator = google_translator(url_suffix=\"hk\",timeout=5,proxies={'https':'206.189.44.99:8080','http':'96.9.77.203:55667'})\n",
    "translator = google_translator(timeout=40)\n",
    "detector = google_translator()  \n",
    "\n",
    "def add_english_translated_tweets_to_df(df):\n",
    "    english_translated_col = []\n",
    "    df['text'] = df['text'].astype(str)\n",
    "    for tweet in df['text']:\n",
    "        if len(tweet)!=0:\n",
    "            if is_detect_lang_required == 1 :\n",
    "                detect_lang = detector.detect(tweet)\n",
    "                if (detect_lang != ['en', 'english']) :\n",
    "                    english_translated_col.append(translatoror.translate(tweet,lang_tgt='en') )\n",
    "                else:\n",
    "                    english_translated_col.append(tweet)\n",
    "            else:\n",
    "                english_translated_col.append(translator.translate(tweet,lang_tgt='en') )\n",
    "        else:\n",
    "            english_translated_col.append(None)\n",
    "\n",
    "    df['english_translated'] = english_translated_col\n",
    "    return df\n",
    "\n",
    "\n",
    "############  Testing block  ##############################################################################\n",
    "#### function to detect lan  ################################\n",
    "text1 = \"Another text which is already in english, so should not be translated.\"\n",
    "text2 = \"È appena uscito un nuovo video! LES CRYPTOMONN...\"\n",
    "detect_lang = detector.detect(text2)\n",
    "print(detect_lang)\n",
    "\n",
    "#### function to translate the text to english ################\n",
    "if (detect_lang != ['en', 'english']) :\n",
    "    print(translator.translate(text2,lang_tgt='en') )\n",
    "else:\n",
    "    print(text2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\"\"\" function for reading, cleaning, filtering over date range, and writing the output to the disk \"\"\"\n",
    "def read_clean_filter_translate_write_file(input_file, no_of_records=no_of_records):\n",
    "    bitcoin_tweets_dataset_df_cleaned = read_and_clean_input_file(input_file, no_of_records)\n",
    "    bitcoin_tweets_dataset_df_cleaned = filter_by_daterange(bitcoin_tweets_dataset_df_cleaned)\n",
    "    \n",
    "    if(is_eng_translator_limit_reached != 1):\n",
    "        bitcoin_tweets_dataset_df_cleaned = add_english_translated_tweets_to_df(bitcoin_tweets_dataset_df_cleaned)\n",
    "    else:        \n",
    "        bitcoin_tweets_dataset_df_cleaned['english_translated'] = bitcoin_tweets_dataset_df_cleaned['text']\n",
    "\n",
    "    ##### Write the processed ouput to disk. This is to avoid unnecssary english translation everytime we run the program\n",
    "    # The seperator delimeter of orginal file is ';' but for processed file it is ','. This should be good across complete file\n",
    "    bitcoin_tweets_dataset_df_cleaned.to_csv(index=True, sep=',' , path_or_buf=processed_bitcoin_tweets_dataset_filepath)\n",
    "    return bitcoin_tweets_dataset_df_cleaned"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Main function of the program"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processed bitcoin_tweets dataset filep exist\n",
      "(2014, 2)\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>english_translated</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-03-07</th>\n",
       "      <td>XCh4NGE lets you decide your exposure to trans...</td>\n",
       "      <td>XCh4NGE lets you decide your exposure to trans...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-21</th>\n",
       "      <td>Top 5 #cryptocurrencies \\n Alert Time: 2019-02...</td>\n",
       "      <td>Top 5 #cryptocurrencies \\n Alert Time: 2019-02...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-18</th>\n",
       "      <td>#cryptocurrency Price Analysis for #Bitsend #B...</td>\n",
       "      <td>#cryptocurrency Price Analysis for #Bitsend #B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-11</th>\n",
       "      <td>Ecex Exchange ICO https://ecex.exchange \\nCurr...</td>\n",
       "      <td>Ecex Exchange ICO https://ecex.exchange \\nCurr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-20</th>\n",
       "      <td>El lunes a las 00:30h en #Millennium ¿Cómo nac...</td>\n",
       "      <td>Monday at 00: 30h in #millennium How is born a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-16</th>\n",
       "      <td>05/17 02:00現在(Zaif調べ)\\n\\n#Bitcoin : 916,000円↓0...</td>\n",
       "      <td>05/17 02:00 Current (ZAIF check)  #Bitcoin: 91...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-01</th>\n",
       "      <td>#BTCUSD Market #1H timeframe on  September 1 a...</td>\n",
       "      <td>#BTCUSD Market #1H timeframe on  September 1 a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-18</th>\n",
       "      <td>Bitcoin 31 Aralığa kadar 20.000 $ geçerse, bu ...</td>\n",
       "      <td>If Bitcoin passes $ 20,000 up to 31 gap, I giv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-18</th>\n",
       "      <td>Together with @nadiaheninger, just presented o...</td>\n",
       "      <td>Together with @nadiaheninger, just presented o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-04</th>\n",
       "      <td>Top 5 #cryptocurrencies \\n Alert Time: 2018-10...</td>\n",
       "      <td>Top 5 #cryptocurrencies \\n Alert Time: 2018-10...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         text  \\\n",
       "timestamp                                                       \n",
       "2019-03-07  XCh4NGE lets you decide your exposure to trans...   \n",
       "2019-02-21  Top 5 #cryptocurrencies \\n Alert Time: 2019-02...   \n",
       "2018-09-18  #cryptocurrency Price Analysis for #Bitsend #B...   \n",
       "2018-03-11  Ecex Exchange ICO https://ecex.exchange \\nCurr...   \n",
       "2018-10-20  El lunes a las 00:30h en #Millennium ¿Cómo nac...   \n",
       "2018-05-16  05/17 02:00現在(Zaif調べ)\\n\\n#Bitcoin : 916,000円↓0...   \n",
       "2018-09-01  #BTCUSD Market #1H timeframe on  September 1 a...   \n",
       "2018-07-18  Bitcoin 31 Aralığa kadar 20.000 $ geçerse, bu ...   \n",
       "2019-02-18  Together with @nadiaheninger, just presented o...   \n",
       "2018-10-04  Top 5 #cryptocurrencies \\n Alert Time: 2018-10...   \n",
       "\n",
       "                                           english_translated  \n",
       "timestamp                                                      \n",
       "2019-03-07  XCh4NGE lets you decide your exposure to trans...  \n",
       "2019-02-21  Top 5 #cryptocurrencies \\n Alert Time: 2019-02...  \n",
       "2018-09-18  #cryptocurrency Price Analysis for #Bitsend #B...  \n",
       "2018-03-11  Ecex Exchange ICO https://ecex.exchange \\nCurr...  \n",
       "2018-10-20  Monday at 00: 30h in #millennium How is born a...  \n",
       "2018-05-16  05/17 02:00 Current (ZAIF check)  #Bitcoin: 91...  \n",
       "2018-09-01  #BTCUSD Market #1H timeframe on  September 1 a...  \n",
       "2018-07-18  If Bitcoin passes $ 20,000 up to 31 gap, I giv...  \n",
       "2019-02-18  Together with @nadiaheninger, just presented o...  \n",
       "2018-10-04  Top 5 #cryptocurrencies \\n Alert Time: 2018-10...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min date is  2017-05-24\n",
      "max date is  2019-03-29\n"
     ]
    }
   ],
   "source": [
    "import os.path\n",
    "from os import path\n",
    "\n",
    "\"\"\" This is the main block of our program. \n",
    "This would check whether it has already processed file, if so simply read from there instead of executing it again \"\"\"\n",
    "def main():\n",
    "    if path.exists(processed_bitcoin_tweets_dataset_filepath):\n",
    "        print (\"processed bitcoin_tweets dataset filep exist\")\n",
    "    else:\n",
    "        print (\"processed bitcoin_tweets dataset file does not exist\")\n",
    "        read_clean_filter_translate_write_file(bitcoin_tweets_dataset_filepath)\n",
    "        \n",
    "    processed_bitcoin_tweets_dataset_df = pd.read_csv(\n",
    "        processed_bitcoin_tweets_dataset_filepath, \n",
    "        sep=',',\n",
    "        dtype= {'text': str, 'english_translated': str},\n",
    "        # dtype= str,\n",
    "        # converters={'english_translated': str},\n",
    "        index_col=[\"timestamp\"])\n",
    "\n",
    "    # Strip time information from the column 'timestamp' if any\n",
    "    # processed_bitcoin_tweets_dataset_df['timestamp'] = pd.to_datetime(processed_bitcoin_tweets_dataset_df['timestamp']).dt.date\n",
    "    processed_bitcoin_tweets_dataset_df.index = pd.Series(pd.to_datetime(processed_bitcoin_tweets_dataset_df.index)).dt.date\n",
    "    print(processed_bitcoin_tweets_dataset_df.shape)\n",
    "    return processed_bitcoin_tweets_dataset_df\n",
    "\n",
    "processed_bitcoin_tweets_dataset_df = main()\n",
    "\n",
    "# Filter the dates between a range which is common to both dataframes - tweets dataframe and price dataframe\n",
    "# processed_bitcoin_tweets_dataset_df = filter_by_daterange(processed_bitcoin_tweets_dataset_df)\n",
    "\n",
    "# processed_bitcoin_tweets_dataset_df = processed_bitcoin_tweets_dataset_df.sort_index()   -- no need to sort. that is just for verification and display purpose, if needed.\n",
    "\n",
    "\n",
    "############  Testing block  ##############################################################################\n",
    "display(processed_bitcoin_tweets_dataset_df.sample(10))\n",
    "print('min date is ',processed_bitcoin_tweets_dataset_df.index.min())\n",
    "print('max date is ',processed_bitcoin_tweets_dataset_df.index.max())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\sk105659\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "import re\n",
    "from nltk.corpus import stopwords\n",
    "nltk.download('stopwords')\n",
    "\n",
    "\"\"\" Utility function to clean tweet text by removing special characters and links using regex\"\"\"\n",
    "def clean_special_chars(text):\n",
    "    return ' '.join(re.sub(\"(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+://\\S+)\", \" \", text).split())\n",
    "\n",
    "\n",
    "\"\"\" Utility function to clean tweet text and remove verbose english words using nltk stopwords\"\"\"\n",
    "def clean_and_remove_english_verbose_words(text):\n",
    "    text = clean_special_chars(text)\n",
    "    forbidden_words = set(stopwords.words('english'))\n",
    "    text = ' '.join(text.split('.'))\n",
    "    text = re.sub('\\/',' ',text)\n",
    "    text = text.strip('\\'\"')\n",
    "    text = re.sub(r'@([^\\s]+)',r'\\1',text)\n",
    "    text = re.sub(r'\\\\',' ',text)\n",
    "    text = text.lower()\n",
    "    text = re.sub('[\\s]+', ' ', text)\n",
    "    text = re.sub(r'#([^\\s]+)', r'\\1', text)\n",
    "    text = re.sub('((www\\.[^\\s]+)|(https?://[^\\s]+))',' ',text)\n",
    "    text = re.sub(r'((http)\\S+)','',text)\n",
    "    text = re.sub(r'\\s+', ' ', re.sub('[^A-Za-z]', ' ', text.strip().lower())).strip()\n",
    "    text = re.sub(r'\\W+', ' ', text.strip().lower()).strip()\n",
    "    text = [word for word in text.split() if word not in forbidden_words]\n",
    "    return ' '.join(text)\n",
    "\n",
    "\n",
    "\"\"\" Utility function to classify sentiment of passed tweet using textblob's sentiment method \"\"\"\n",
    "def sentiment(text):\n",
    "    # create TextBlob object of passed tweet text\n",
    "    text_analysis = TextBlob(text)\n",
    "    return text_analysis.sentiment.polarity\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>english_translated</th>\n",
       "      <th>english_keywords</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-05-24</th>\n",
       "      <td>So @Bitcoin just broke $2400.00 ea thanks to J...</td>\n",
       "      <td>So @Bitcoin just broke $2400.00 ea thanks to J...</td>\n",
       "      <td>broke ea thanks japan legislation good day enc...</td>\n",
       "      <td>0.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-25</th>\n",
       "      <td>#Bitcoin #thatBitcoinlife will provide everyth...</td>\n",
       "      <td>#Bitcoin #thatBitcoinlife will provide everyth...</td>\n",
       "      <td>bitcoin thatbitcoinlife provide everything tho...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-06</th>\n",
       "      <td>New all time #bitstamp #bitcoin high of $2787.00</td>\n",
       "      <td>New all time #bitstamp #bitcoin high of $2787.00</td>\n",
       "      <td>new time bitstamp bitcoin high</td>\n",
       "      <td>0.148182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-22</th>\n",
       "      <td>JNECare: wprameswari_15 Hi kak, admin cek kiri...</td>\n",
       "      <td>JNE Care: PRAMESWARI 15 HI Sis, Admin Checks t...</td>\n",
       "      <td>jne care prameswari hi sis admin checks shipme...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-30</th>\n",
       "      <td>historic btc price on July 4th\\n2016: +6.00% \\...</td>\n",
       "      <td>historic btc price on July 4th\\n2016: +6.00% \\...</td>\n",
       "      <td>historic btc price july th</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-25</th>\n",
       "      <td>Oh shit! Still time to buy $btc for lunch in 1...</td>\n",
       "      <td>Oh shit! Still time to buy $btc for lunch in 1...</td>\n",
       "      <td>oh shit still time buy btc lunch min every day...</td>\n",
       "      <td>-0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-26</th>\n",
       "      <td>₿1 = $3,951 (00:56 UTC)\\n$BTC prices rise!\\n#...</td>\n",
       "      <td>#cryptocurrency Price Analysis for #Bitsend #B...</td>\n",
       "      <td>bittrex volume changed globalcoin btc glc pric...</td>\n",
       "      <td>-0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-27</th>\n",
       "      <td>#BTT در @Coinsuper_OFCL در دسترس است، شما میتو...</td>\n",
       "      <td>#BTT was listed in @CoInSuper_OFCL. #BTT can b...</td>\n",
       "      <td>bittrex volume changed bitstarcoin btc bits pr...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-28</th>\n",
       "      <td>And we deserved it :)\\n\\n#agrocoin #LATOKEN #a...</td>\n",
       "      <td>And we deserved it :)\\n\\n#agrocoin #LATOKEN #a...</td>\n",
       "      <td>bittrex volume changed folding coin btc fldc p...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-29</th>\n",
       "      <td>₿1 = $4,136 (00:28 UTC)\\n$BTC prices continue...</td>\n",
       "      <td>#cryptocurrency Price Analysis for #Bitsend #B...</td>\n",
       "      <td>bittrex volume changed counterparty btc xcp pr...</td>\n",
       "      <td>-0.221212</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>463 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         text  \\\n",
       "timestamp                                                       \n",
       "2017-05-24  So @Bitcoin just broke $2400.00 ea thanks to J...   \n",
       "2017-05-25  #Bitcoin #thatBitcoinlife will provide everyth...   \n",
       "2017-06-06   New all time #bitstamp #bitcoin high of $2787.00   \n",
       "2017-06-22  JNECare: wprameswari_15 Hi kak, admin cek kiri...   \n",
       "2017-06-30  historic btc price on July 4th\\n2016: +6.00% \\...   \n",
       "...                                                       ...   \n",
       "2019-03-25  Oh shit! Still time to buy $btc for lunch in 1...   \n",
       "2019-03-26   ₿1 = $3,951 (00:56 UTC)\\n$BTC prices rise!\\n#...   \n",
       "2019-03-27  #BTT در @Coinsuper_OFCL در دسترس است، شما میتو...   \n",
       "2019-03-28  And we deserved it :)\\n\\n#agrocoin #LATOKEN #a...   \n",
       "2019-03-29   ₿1 = $4,136 (00:28 UTC)\\n$BTC prices continue...   \n",
       "\n",
       "                                           english_translated  \\\n",
       "timestamp                                                       \n",
       "2017-05-24  So @Bitcoin just broke $2400.00 ea thanks to J...   \n",
       "2017-05-25  #Bitcoin #thatBitcoinlife will provide everyth...   \n",
       "2017-06-06  New all time #bitstamp #bitcoin high of $2787.00    \n",
       "2017-06-22  JNE Care: PRAMESWARI 15 HI Sis, Admin Checks t...   \n",
       "2017-06-30  historic btc price on July 4th\\n2016: +6.00% \\...   \n",
       "...                                                       ...   \n",
       "2019-03-25  Oh shit! Still time to buy $btc for lunch in 1...   \n",
       "2019-03-26  #cryptocurrency Price Analysis for #Bitsend #B...   \n",
       "2019-03-27  #BTT was listed in @CoInSuper_OFCL. #BTT can b...   \n",
       "2019-03-28  And we deserved it :)\\n\\n#agrocoin #LATOKEN #a...   \n",
       "2019-03-29  #cryptocurrency Price Analysis for #Bitsend #B...   \n",
       "\n",
       "                                             english_keywords  sentiment  \n",
       "timestamp                                                                 \n",
       "2017-05-24  broke ea thanks japan legislation good day enc...   0.450000  \n",
       "2017-05-25  bitcoin thatbitcoinlife provide everything tho...   0.000000  \n",
       "2017-06-06                     new time bitstamp bitcoin high   0.148182  \n",
       "2017-06-22  jne care prameswari hi sis admin checks shipme...   0.000000  \n",
       "2017-06-30                         historic btc price july th   0.000000  \n",
       "...                                                       ...        ...  \n",
       "2019-03-25  oh shit still time buy btc lunch min every day...  -0.250000  \n",
       "2019-03-26  bittrex volume changed globalcoin btc glc pric...  -0.500000  \n",
       "2019-03-27  bittrex volume changed bitstarcoin btc bits pr...   0.000000  \n",
       "2019-03-28  bittrex volume changed folding coin btc fldc p...   0.000000  \n",
       "2019-03-29  bittrex volume changed counterparty btc xcp pr...  -0.221212  \n",
       "\n",
       "[463 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from textblob import TextBlob\n",
    "\n",
    "\"\"\" Utility function to classify sentiment of passed tweet using textblob's sentiment method \"\"\"\n",
    "def add_eng_keywords_and_sentiments(df):\n",
    "    english_keywords_col = []\n",
    "    sentiment_col = []\n",
    "    for tweet in df['english_translated']:\n",
    "        \n",
    "        # If running stopwords on big data slows donw the speed, then we can skip stopwords logic for performance and only use the simple cleaning of special chars\n",
    "        english_keywords = clean_and_remove_english_verbose_words(tweet)\n",
    "        #   english_keywords = clean_special_chars(tweet)\n",
    "        english_keywords_col.append(english_keywords)\n",
    "        \n",
    "        # Add corresponding sentiments\n",
    "        sentiment_polarity = sentiment(tweet)\n",
    "        sentiment_col.append(sentiment_polarity)\n",
    "    \n",
    "    df['english_keywords'] = english_keywords_col\n",
    "    df['sentiment'] = sentiment_col\n",
    "    return df\n",
    "\n",
    "processed_bitcoin_tweets_dataset_df = add_eng_keywords_and_sentiments(processed_bitcoin_tweets_dataset_df)\n",
    "\n",
    "\n",
    "# Considering minimum sentiment of the day.\n",
    "#  Note : played with first, max, min, mean. i think mean is the best one for this.\n",
    "bitcoin_tweets_sentiment_df = processed_bitcoin_tweets_dataset_df.groupby(processed_bitcoin_tweets_dataset_df.index).min()\n",
    "bitcoin_tweets_sentiment_df.sample(10)\n",
    "bitcoin_tweets_sentiment_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Referring to part1 notebook for already proccessed bitcoin price dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Change</th>\n",
       "      <th>coin_name_abbrv</th>\n",
       "      <th>coin_name</th>\n",
       "      <th>Volume_in_Million</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-04-25</th>\n",
       "      <td>49561.9</td>\n",
       "      <td>50088.2</td>\n",
       "      <td>50438.8</td>\n",
       "      <td>49226.5</td>\n",
       "      <td>66.26K</td>\n",
       "      <td>-1.05%</td>\n",
       "      <td>Bit</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>0.06626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-24</th>\n",
       "      <td>50088.9</td>\n",
       "      <td>51140.8</td>\n",
       "      <td>51183.0</td>\n",
       "      <td>48775.2</td>\n",
       "      <td>82.25K</td>\n",
       "      <td>-2.06%</td>\n",
       "      <td>Bit</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>0.08225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-23</th>\n",
       "      <td>51143.6</td>\n",
       "      <td>51707.1</td>\n",
       "      <td>52099.9</td>\n",
       "      <td>47659.4</td>\n",
       "      <td>214.46K</td>\n",
       "      <td>-1.13%</td>\n",
       "      <td>Bit</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>0.21446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-22</th>\n",
       "      <td>51729.5</td>\n",
       "      <td>53821.3</td>\n",
       "      <td>55408.4</td>\n",
       "      <td>50590.9</td>\n",
       "      <td>168.13K</td>\n",
       "      <td>-3.88%</td>\n",
       "      <td>Bit</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>0.16813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-21</th>\n",
       "      <td>53820.2</td>\n",
       "      <td>56479.5</td>\n",
       "      <td>56764.4</td>\n",
       "      <td>53657.6</td>\n",
       "      <td>100.26K</td>\n",
       "      <td>-4.71%</td>\n",
       "      <td>Bit</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>0.10026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-22</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2.16K</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>Bit</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>0.00216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-21</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.58K</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>Bit</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>0.00058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-20</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.26K</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>Bit</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>0.00026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-19</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.57K</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>Bit</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>0.00057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-18</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.08K</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>Bit</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>0.00008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3935 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Price     Open     High      Low   Volume  Change  \\\n",
       "Date                                                              \n",
       "2021-04-25  49561.9  50088.2  50438.8  49226.5   66.26K  -1.05%   \n",
       "2021-04-24  50088.9  51140.8  51183.0  48775.2   82.25K  -2.06%   \n",
       "2021-04-23  51143.6  51707.1  52099.9  47659.4  214.46K  -1.13%   \n",
       "2021-04-22  51729.5  53821.3  55408.4  50590.9  168.13K  -3.88%   \n",
       "2021-04-21  53820.2  56479.5  56764.4  53657.6  100.26K  -4.71%   \n",
       "...             ...      ...      ...      ...      ...     ...   \n",
       "2010-07-22      0.1      0.1      0.1      0.1    2.16K   0.00%   \n",
       "2010-07-21      0.1      0.1      0.1      0.1    0.58K   0.00%   \n",
       "2010-07-20      0.1      0.1      0.1      0.1    0.26K   0.00%   \n",
       "2010-07-19      0.1      0.1      0.1      0.1    0.57K   0.00%   \n",
       "2010-07-18      0.1      0.0      0.1      0.1    0.08K   0.00%   \n",
       "\n",
       "           coin_name_abbrv coin_name  Volume_in_Million  \n",
       "Date                                                     \n",
       "2021-04-25             Bit   Bitcoin            0.06626  \n",
       "2021-04-24             Bit   Bitcoin            0.08225  \n",
       "2021-04-23             Bit   Bitcoin            0.21446  \n",
       "2021-04-22             Bit   Bitcoin            0.16813  \n",
       "2021-04-21             Bit   Bitcoin            0.10026  \n",
       "...                    ...       ...                ...  \n",
       "2010-07-22             Bit   Bitcoin            0.00216  \n",
       "2010-07-21             Bit   Bitcoin            0.00058  \n",
       "2010-07-20             Bit   Bitcoin            0.00026  \n",
       "2010-07-19             Bit   Bitcoin            0.00057  \n",
       "2010-07-18             Bit   Bitcoin            0.00008  \n",
       "\n",
       "[3935 rows x 9 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-03-29</th>\n",
       "      <td>4102.2</td>\n",
       "      <td>1.90%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-28</th>\n",
       "      <td>4025.6</td>\n",
       "      <td>-0.40%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-27</th>\n",
       "      <td>4041.7</td>\n",
       "      <td>2.51%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-26</th>\n",
       "      <td>3942.8</td>\n",
       "      <td>0.15%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-25</th>\n",
       "      <td>3937.0</td>\n",
       "      <td>-1.44%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-05</th>\n",
       "      <td>1271.2</td>\n",
       "      <td>0.55%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-04</th>\n",
       "      <td>1264.3</td>\n",
       "      <td>-1.48%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-03</th>\n",
       "      <td>1283.3</td>\n",
       "      <td>2.22%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-02</th>\n",
       "      <td>1255.5</td>\n",
       "      <td>2.68%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-01</th>\n",
       "      <td>1222.7</td>\n",
       "      <td>2.81%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>759 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Price  Change\n",
       "Date                      \n",
       "2019-03-29  4102.2   1.90%\n",
       "2019-03-28  4025.6  -0.40%\n",
       "2019-03-27  4041.7   2.51%\n",
       "2019-03-26  3942.8   0.15%\n",
       "2019-03-25  3937.0  -1.44%\n",
       "...            ...     ...\n",
       "2017-03-05  1271.2   0.55%\n",
       "2017-03-04  1264.3  -1.48%\n",
       "2017-03-03  1283.3   2.22%\n",
       "2017-03-02  1255.5   2.68%\n",
       "2017-03-01  1222.7   2.81%\n",
       "\n",
       "[759 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "######## Part1 notebook is writing processed output to a file and this part 2 is reading the same file #################\n",
    "# %store -r big_frame\n",
    "# display(big_frame)\n",
    "# bitcoin_price_df = big_frame.query(\"coin_name == 'Bitcoin'\")\n",
    "# bitcoin_price_df.to_csv(index=False, sep=',' , path_or_buf=processed_bitcoin_price_filepath)\n",
    "# display(bitcoin_price_df)\n",
    "\n",
    "\n",
    "\"\"\" function to read and filter over the date range. \n",
    "This date range is common to both dataframes - tweets dataframe and price dataframe \"\"\"\n",
    "def read_and_filter_bitcoin_price_file():\n",
    "    bitcoin_price_df = pd.read_csv(processed_bitcoin_price_filepath, sep=',')\n",
    "    bitcoin_price_df['Date'] = pd.to_datetime(bitcoin_price_df['Date']).dt.date\n",
    "    bitcoin_price_df = bitcoin_price_df.set_index(['Date'])\n",
    "    display(bitcoin_price_df)\n",
    "\n",
    "    # Filter over date range. \n",
    "    bitcoin_price_df = filter_by_daterange(bitcoin_price_df)\n",
    "    # bitcoin_price_df = bitcoin_price_df['Price'].to_frame()\n",
    "    bitcoin_price_df = bitcoin_price_df[['Price', 'Change']]\n",
    "    return bitcoin_price_df\n",
    "\n",
    "bitcoin_price_df = read_and_filter_bitcoin_price_file()\n",
    "display(bitcoin_price_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Change</th>\n",
       "      <th>text</th>\n",
       "      <th>english_translated</th>\n",
       "      <th>english_keywords</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>sentiment_scaled</th>\n",
       "      <th>sentiment_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-03-29</th>\n",
       "      <td>4102.2</td>\n",
       "      <td>1.90%</td>\n",
       "      <td>₿1 = $4,136 (00:28 UTC)\\n$BTC prices continue...</td>\n",
       "      <td>#cryptocurrency Price Analysis for #Bitsend #B...</td>\n",
       "      <td>bittrex volume changed counterparty btc xcp pr...</td>\n",
       "      <td>-0.221212</td>\n",
       "      <td>-2212.121212</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-28</th>\n",
       "      <td>4025.6</td>\n",
       "      <td>-0.40%</td>\n",
       "      <td>And we deserved it :)\\n\\n#agrocoin #LATOKEN #a...</td>\n",
       "      <td>And we deserved it :)\\n\\n#agrocoin #LATOKEN #a...</td>\n",
       "      <td>bittrex volume changed folding coin btc fldc p...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-27</th>\n",
       "      <td>4041.7</td>\n",
       "      <td>2.51%</td>\n",
       "      <td>#BTT در @Coinsuper_OFCL در دسترس است، شما میتو...</td>\n",
       "      <td>#BTT was listed in @CoInSuper_OFCL. #BTT can b...</td>\n",
       "      <td>bittrex volume changed bitstarcoin btc bits pr...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-26</th>\n",
       "      <td>3942.8</td>\n",
       "      <td>0.15%</td>\n",
       "      <td>₿1 = $3,951 (00:56 UTC)\\n$BTC prices rise!\\n#...</td>\n",
       "      <td>#cryptocurrency Price Analysis for #Bitsend #B...</td>\n",
       "      <td>bittrex volume changed globalcoin btc glc pric...</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-5000.000000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-25</th>\n",
       "      <td>3937.0</td>\n",
       "      <td>-1.44%</td>\n",
       "      <td>Oh shit! Still time to buy $btc for lunch in 1...</td>\n",
       "      <td>Oh shit! Still time to buy $btc for lunch in 1...</td>\n",
       "      <td>oh shit still time buy btc lunch min every day...</td>\n",
       "      <td>-0.250000</td>\n",
       "      <td>-2500.000000</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-30</th>\n",
       "      <td>2480.6</td>\n",
       "      <td>-3.04%</td>\n",
       "      <td>historic btc price on July 4th\\n2016: +6.00% \\...</td>\n",
       "      <td>historic btc price on July 4th\\n2016: +6.00% \\...</td>\n",
       "      <td>historic btc price july th</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-22</th>\n",
       "      <td>2722.8</td>\n",
       "      <td>1.69%</td>\n",
       "      <td>JNECare: wprameswari_15 Hi kak, admin cek kiri...</td>\n",
       "      <td>JNE Care: PRAMESWARI 15 HI Sis, Admin Checks t...</td>\n",
       "      <td>jne care prameswari hi sis admin checks shipme...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-06</th>\n",
       "      <td>2870.5</td>\n",
       "      <td>6.12%</td>\n",
       "      <td>New all time #bitstamp #bitcoin high of $2787.00</td>\n",
       "      <td>New all time #bitstamp #bitcoin high of $2787.00</td>\n",
       "      <td>new time bitstamp bitcoin high</td>\n",
       "      <td>0.148182</td>\n",
       "      <td>1481.818182</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-25</th>\n",
       "      <td>2307.2</td>\n",
       "      <td>-5.65%</td>\n",
       "      <td>#Bitcoin #thatBitcoinlife will provide everyth...</td>\n",
       "      <td>#Bitcoin #thatBitcoinlife will provide everyth...</td>\n",
       "      <td>bitcoin thatbitcoinlife provide everything tho...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-24</th>\n",
       "      <td>2445.3</td>\n",
       "      <td>7.60%</td>\n",
       "      <td>So @Bitcoin just broke $2400.00 ea thanks to J...</td>\n",
       "      <td>So @Bitcoin just broke $2400.00 ea thanks to J...</td>\n",
       "      <td>broke ea thanks japan legislation good day enc...</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>4500.000000</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>463 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Price  Change                                               text  \\\n",
       "2019-03-29  4102.2   1.90%   ₿1 = $4,136 (00:28 UTC)\\n$BTC prices continue...   \n",
       "2019-03-28  4025.6  -0.40%  And we deserved it :)\\n\\n#agrocoin #LATOKEN #a...   \n",
       "2019-03-27  4041.7   2.51%  #BTT در @Coinsuper_OFCL در دسترس است، شما میتو...   \n",
       "2019-03-26  3942.8   0.15%   ₿1 = $3,951 (00:56 UTC)\\n$BTC prices rise!\\n#...   \n",
       "2019-03-25  3937.0  -1.44%  Oh shit! Still time to buy $btc for lunch in 1...   \n",
       "...            ...     ...                                                ...   \n",
       "2017-06-30  2480.6  -3.04%  historic btc price on July 4th\\n2016: +6.00% \\...   \n",
       "2017-06-22  2722.8   1.69%  JNECare: wprameswari_15 Hi kak, admin cek kiri...   \n",
       "2017-06-06  2870.5   6.12%   New all time #bitstamp #bitcoin high of $2787.00   \n",
       "2017-05-25  2307.2  -5.65%  #Bitcoin #thatBitcoinlife will provide everyth...   \n",
       "2017-05-24  2445.3   7.60%  So @Bitcoin just broke $2400.00 ea thanks to J...   \n",
       "\n",
       "                                           english_translated  \\\n",
       "2019-03-29  #cryptocurrency Price Analysis for #Bitsend #B...   \n",
       "2019-03-28  And we deserved it :)\\n\\n#agrocoin #LATOKEN #a...   \n",
       "2019-03-27  #BTT was listed in @CoInSuper_OFCL. #BTT can b...   \n",
       "2019-03-26  #cryptocurrency Price Analysis for #Bitsend #B...   \n",
       "2019-03-25  Oh shit! Still time to buy $btc for lunch in 1...   \n",
       "...                                                       ...   \n",
       "2017-06-30  historic btc price on July 4th\\n2016: +6.00% \\...   \n",
       "2017-06-22  JNE Care: PRAMESWARI 15 HI Sis, Admin Checks t...   \n",
       "2017-06-06  New all time #bitstamp #bitcoin high of $2787.00    \n",
       "2017-05-25  #Bitcoin #thatBitcoinlife will provide everyth...   \n",
       "2017-05-24  So @Bitcoin just broke $2400.00 ea thanks to J...   \n",
       "\n",
       "                                             english_keywords  sentiment  \\\n",
       "2019-03-29  bittrex volume changed counterparty btc xcp pr...  -0.221212   \n",
       "2019-03-28  bittrex volume changed folding coin btc fldc p...   0.000000   \n",
       "2019-03-27  bittrex volume changed bitstarcoin btc bits pr...   0.000000   \n",
       "2019-03-26  bittrex volume changed globalcoin btc glc pric...  -0.500000   \n",
       "2019-03-25  oh shit still time buy btc lunch min every day...  -0.250000   \n",
       "...                                                       ...        ...   \n",
       "2017-06-30                         historic btc price july th   0.000000   \n",
       "2017-06-22  jne care prameswari hi sis admin checks shipme...   0.000000   \n",
       "2017-06-06                     new time bitstamp bitcoin high   0.148182   \n",
       "2017-05-25  bitcoin thatbitcoinlife provide everything tho...   0.000000   \n",
       "2017-05-24  broke ea thanks japan legislation good day enc...   0.450000   \n",
       "\n",
       "            sentiment_scaled  sentiment_cat  \n",
       "2019-03-29      -2212.121212             17  \n",
       "2019-03-28          0.000000             50  \n",
       "2019-03-27          0.000000             50  \n",
       "2019-03-26      -5000.000000              4  \n",
       "2019-03-25      -2500.000000             15  \n",
       "...                      ...            ...  \n",
       "2017-06-30          0.000000             50  \n",
       "2017-06-22          0.000000             50  \n",
       "2017-06-06       1481.818182             59  \n",
       "2017-05-25          0.000000             50  \n",
       "2017-05-24       4500.000000             80  \n",
       "\n",
       "[463 rows x 8 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\" function to merge two dataframes based on common date range \"\"\"\n",
    "def merge_bitcoin_price_and_tweets_data(bitcoin_price_df, bitcoin_tweets_sentiment_df):\n",
    "    df = pd.merge(bitcoin_price_df, bitcoin_tweets_sentiment_df, left_index=True, right_index=True, how='inner')\n",
    "    return df\n",
    "\n",
    "\"\"\" function to add  'Sentiment categorical' column \"\"\"\n",
    "def make_sentiment_column_categorical(bitcoin_price_sentiment):\n",
    "    bitcoin_price_sentiment['sentiment_cat'] = bitcoin_price_sentiment['sentiment'].astype('category')\n",
    "    bitcoin_price_sentiment['sentiment_cat'] = bitcoin_price_sentiment['sentiment_cat'].cat.codes\n",
    "    return bitcoin_price_sentiment\n",
    "\n",
    "bitcoin_price_sentiment  = merge_bitcoin_price_and_tweets_data(bitcoin_price_df, bitcoin_tweets_sentiment_df)\n",
    "bitcoin_price_sentiment['sentiment_scaled'] = bitcoin_price_sentiment['sentiment'] * 10000\n",
    "bitcoin_price_sentiment = make_sentiment_column_categorical(bitcoin_price_sentiment)\n",
    "bitcoin_price_sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\"\"\" function to plot line  \"\"\"\n",
    "def plot_one_line(line1, label1=None, title='', color='blue', xlabel=None, ylabel=None, legend_loc='best', lw=2):\n",
    "    fig, ax = plt.subplots(1, figsize=(13, 7))\n",
    "    ax.plot(line1, label=label1, linewidth=lw, color=color)\n",
    "    ax.set_xlabel(xlabel, fontsize=14)\n",
    "    ax.set_ylabel(ylabel, fontsize=14)\n",
    "    ax.set_title(title, fontsize=16)\n",
    "    ax.legend(loc=legend_loc, fontsize=16)\n",
    "    \n",
    "\"\"\" function to plot two lines in one figuare  \"\"\"\n",
    "def plot_two_lines(line1, line2, label1=None, label2=None, title='', xlabel=None, ylabel=None, legend_loc='best', lw=2):\n",
    "    fig, ax = plt.subplots(1, figsize=(13, 7))\n",
    "    ax.plot(line1, label=label1, linewidth=lw)\n",
    "    ax.plot(line2, label=label2, linewidth=lw)\n",
    "    ax.set_xlabel(xlabel, fontsize=14)\n",
    "    ax.set_ylabel(ylabel, fontsize=14)\n",
    "    ax.set_title(title, fontsize=16)\n",
    "    ax.legend(loc=legend_loc, fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_one_line(bitcoin_price_sentiment['Price'], 'price', title='Analysing bitcoin price ', color ='blue')\n",
    "plot_one_line(bitcoin_price_sentiment['sentiment'] , 'sentiment', title='Analysing bitcoin tweets sentiments', color = 'orange')\n",
    "# plot_one_line(bitcoin_price_sentiment['sentiment_scaled'] , 'sentiment_scaled', title='Analysing tweets sentiments', color = 'orange')\n",
    "# plot_one_line(bitcoin_price_sentiment['sentiment_cat'] , 'sentiment_cat', title='Analysing tweets sentiments', color = 'orange')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_two_lines(bitcoin_price_sentiment['Price'], bitcoin_price_sentiment['sentiment_scaled'], 'price', 'sentiment_scaled', title='Analysing tweet\\'s sentiments impact on bitcoin price ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Change</th>\n",
       "      <th>text</th>\n",
       "      <th>english_translated</th>\n",
       "      <th>english_keywords</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>sentiment_scaled</th>\n",
       "      <th>sentiment_cat</th>\n",
       "      <th>sentiment_nature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-03-29</th>\n",
       "      <td>4102.2</td>\n",
       "      <td>1.90%</td>\n",
       "      <td>₿1 = $4,136 (00:28 UTC)\\n$BTC prices continue...</td>\n",
       "      <td>#cryptocurrency Price Analysis for #Bitsend #B...</td>\n",
       "      <td>bittrex volume changed counterparty btc xcp pr...</td>\n",
       "      <td>-0.221212</td>\n",
       "      <td>-2212.121212</td>\n",
       "      <td>17</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-28</th>\n",
       "      <td>4025.6</td>\n",
       "      <td>-0.40%</td>\n",
       "      <td>And we deserved it :)\\n\\n#agrocoin #LATOKEN #a...</td>\n",
       "      <td>And we deserved it :)\\n\\n#agrocoin #LATOKEN #a...</td>\n",
       "      <td>bittrex volume changed folding coin btc fldc p...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-27</th>\n",
       "      <td>4041.7</td>\n",
       "      <td>2.51%</td>\n",
       "      <td>#BTT در @Coinsuper_OFCL در دسترس است، شما میتو...</td>\n",
       "      <td>#BTT was listed in @CoInSuper_OFCL. #BTT can b...</td>\n",
       "      <td>bittrex volume changed bitstarcoin btc bits pr...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-26</th>\n",
       "      <td>3942.8</td>\n",
       "      <td>0.15%</td>\n",
       "      <td>₿1 = $3,951 (00:56 UTC)\\n$BTC prices rise!\\n#...</td>\n",
       "      <td>#cryptocurrency Price Analysis for #Bitsend #B...</td>\n",
       "      <td>bittrex volume changed globalcoin btc glc pric...</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-5000.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-25</th>\n",
       "      <td>3937.0</td>\n",
       "      <td>-1.44%</td>\n",
       "      <td>Oh shit! Still time to buy $btc for lunch in 1...</td>\n",
       "      <td>Oh shit! Still time to buy $btc for lunch in 1...</td>\n",
       "      <td>oh shit still time buy btc lunch min every day...</td>\n",
       "      <td>-0.250000</td>\n",
       "      <td>-2500.000000</td>\n",
       "      <td>15</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-30</th>\n",
       "      <td>2480.6</td>\n",
       "      <td>-3.04%</td>\n",
       "      <td>historic btc price on July 4th\\n2016: +6.00% \\...</td>\n",
       "      <td>historic btc price on July 4th\\n2016: +6.00% \\...</td>\n",
       "      <td>historic btc price july th</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-22</th>\n",
       "      <td>2722.8</td>\n",
       "      <td>1.69%</td>\n",
       "      <td>JNECare: wprameswari_15 Hi kak, admin cek kiri...</td>\n",
       "      <td>JNE Care: PRAMESWARI 15 HI Sis, Admin Checks t...</td>\n",
       "      <td>jne care prameswari hi sis admin checks shipme...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-06</th>\n",
       "      <td>2870.5</td>\n",
       "      <td>6.12%</td>\n",
       "      <td>New all time #bitstamp #bitcoin high of $2787.00</td>\n",
       "      <td>New all time #bitstamp #bitcoin high of $2787.00</td>\n",
       "      <td>new time bitstamp bitcoin high</td>\n",
       "      <td>0.148182</td>\n",
       "      <td>1481.818182</td>\n",
       "      <td>59</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-25</th>\n",
       "      <td>2307.2</td>\n",
       "      <td>-5.65%</td>\n",
       "      <td>#Bitcoin #thatBitcoinlife will provide everyth...</td>\n",
       "      <td>#Bitcoin #thatBitcoinlife will provide everyth...</td>\n",
       "      <td>bitcoin thatbitcoinlife provide everything tho...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-24</th>\n",
       "      <td>2445.3</td>\n",
       "      <td>7.60%</td>\n",
       "      <td>So @Bitcoin just broke $2400.00 ea thanks to J...</td>\n",
       "      <td>So @Bitcoin just broke $2400.00 ea thanks to J...</td>\n",
       "      <td>broke ea thanks japan legislation good day enc...</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>4500.000000</td>\n",
       "      <td>80</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>463 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Price  Change                                               text  \\\n",
       "2019-03-29  4102.2   1.90%   ₿1 = $4,136 (00:28 UTC)\\n$BTC prices continue...   \n",
       "2019-03-28  4025.6  -0.40%  And we deserved it :)\\n\\n#agrocoin #LATOKEN #a...   \n",
       "2019-03-27  4041.7   2.51%  #BTT در @Coinsuper_OFCL در دسترس است، شما میتو...   \n",
       "2019-03-26  3942.8   0.15%   ₿1 = $3,951 (00:56 UTC)\\n$BTC prices rise!\\n#...   \n",
       "2019-03-25  3937.0  -1.44%  Oh shit! Still time to buy $btc for lunch in 1...   \n",
       "...            ...     ...                                                ...   \n",
       "2017-06-30  2480.6  -3.04%  historic btc price on July 4th\\n2016: +6.00% \\...   \n",
       "2017-06-22  2722.8   1.69%  JNECare: wprameswari_15 Hi kak, admin cek kiri...   \n",
       "2017-06-06  2870.5   6.12%   New all time #bitstamp #bitcoin high of $2787.00   \n",
       "2017-05-25  2307.2  -5.65%  #Bitcoin #thatBitcoinlife will provide everyth...   \n",
       "2017-05-24  2445.3   7.60%  So @Bitcoin just broke $2400.00 ea thanks to J...   \n",
       "\n",
       "                                           english_translated  \\\n",
       "2019-03-29  #cryptocurrency Price Analysis for #Bitsend #B...   \n",
       "2019-03-28  And we deserved it :)\\n\\n#agrocoin #LATOKEN #a...   \n",
       "2019-03-27  #BTT was listed in @CoInSuper_OFCL. #BTT can b...   \n",
       "2019-03-26  #cryptocurrency Price Analysis for #Bitsend #B...   \n",
       "2019-03-25  Oh shit! Still time to buy $btc for lunch in 1...   \n",
       "...                                                       ...   \n",
       "2017-06-30  historic btc price on July 4th\\n2016: +6.00% \\...   \n",
       "2017-06-22  JNE Care: PRAMESWARI 15 HI Sis, Admin Checks t...   \n",
       "2017-06-06  New all time #bitstamp #bitcoin high of $2787.00    \n",
       "2017-05-25  #Bitcoin #thatBitcoinlife will provide everyth...   \n",
       "2017-05-24  So @Bitcoin just broke $2400.00 ea thanks to J...   \n",
       "\n",
       "                                             english_keywords  sentiment  \\\n",
       "2019-03-29  bittrex volume changed counterparty btc xcp pr...  -0.221212   \n",
       "2019-03-28  bittrex volume changed folding coin btc fldc p...   0.000000   \n",
       "2019-03-27  bittrex volume changed bitstarcoin btc bits pr...   0.000000   \n",
       "2019-03-26  bittrex volume changed globalcoin btc glc pric...  -0.500000   \n",
       "2019-03-25  oh shit still time buy btc lunch min every day...  -0.250000   \n",
       "...                                                       ...        ...   \n",
       "2017-06-30                         historic btc price july th   0.000000   \n",
       "2017-06-22  jne care prameswari hi sis admin checks shipme...   0.000000   \n",
       "2017-06-06                     new time bitstamp bitcoin high   0.148182   \n",
       "2017-05-25  bitcoin thatbitcoinlife provide everything tho...   0.000000   \n",
       "2017-05-24  broke ea thanks japan legislation good day enc...   0.450000   \n",
       "\n",
       "            sentiment_scaled  sentiment_cat sentiment_nature  \n",
       "2019-03-29      -2212.121212             17         negative  \n",
       "2019-03-28          0.000000             50          neutral  \n",
       "2019-03-27          0.000000             50          neutral  \n",
       "2019-03-26      -5000.000000              4         negative  \n",
       "2019-03-25      -2500.000000             15         negative  \n",
       "...                      ...            ...              ...  \n",
       "2017-06-30          0.000000             50          neutral  \n",
       "2017-06-22          0.000000             50          neutral  \n",
       "2017-06-06       1481.818182             59         positive  \n",
       "2017-05-25          0.000000             50          neutral  \n",
       "2017-05-24       4500.000000             80         positive  \n",
       "\n",
       "[463 rows x 9 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# bitcoin_price_sentiment\n",
    "\n",
    "\"\"\" function to find sentiment nature (positive, negative or neutral) from sentiment polarity \"\"\"\n",
    "def get_tweet_sentiment_nature(tweet_polarity):\n",
    "    # set sentiment\n",
    "    if tweet_polarity > 0:\n",
    "        return 'positive'\n",
    "    elif tweet_polarity == 0:\n",
    "        return 'neutral'\n",
    "    else:\n",
    "        return 'negative'\n",
    "    \n",
    "bitcoin_price_sentiment['sentiment_nature'] = bitcoin_price_sentiment['sentiment'].apply(lambda tweet_polarity: get_tweet_sentiment_nature(tweet_polarity))    \n",
    "bitcoin_price_sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Cleaned tweets count =  463\n",
      "\n",
      " Cleaned bitcoin_price count =  759\n",
      "\n",
      " Cleaned merged df count =  463\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Change</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>sentiment_cat</th>\n",
       "      <th>sentiment_nature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-03-29</th>\n",
       "      <td>1.90</td>\n",
       "      <td>-0.221212</td>\n",
       "      <td>17</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-28</th>\n",
       "      <td>-0.40</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-27</th>\n",
       "      <td>2.51</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-26</th>\n",
       "      <td>0.15</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>4</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-25</th>\n",
       "      <td>-1.44</td>\n",
       "      <td>-0.250000</td>\n",
       "      <td>15</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-30</th>\n",
       "      <td>-3.04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-22</th>\n",
       "      <td>1.69</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-06</th>\n",
       "      <td>6.12</td>\n",
       "      <td>0.148182</td>\n",
       "      <td>59</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-25</th>\n",
       "      <td>-5.65</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-24</th>\n",
       "      <td>7.60</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>80</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>463 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Change  sentiment  sentiment_cat sentiment_nature\n",
       "2019-03-29    1.90  -0.221212             17         negative\n",
       "2019-03-28   -0.40   0.000000             50          neutral\n",
       "2019-03-27    2.51   0.000000             50          neutral\n",
       "2019-03-26    0.15  -0.500000              4         negative\n",
       "2019-03-25   -1.44  -0.250000             15         negative\n",
       "...            ...        ...            ...              ...\n",
       "2017-06-30   -3.04   0.000000             50          neutral\n",
       "2017-06-22    1.69   0.000000             50          neutral\n",
       "2017-06-06    6.12   0.148182             59         positive\n",
       "2017-05-25   -5.65   0.000000             50          neutral\n",
       "2017-05-24    7.60   0.450000             80         positive\n",
       "\n",
       "[463 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"\\n Cleaned tweets count = \", bitcoin_tweets_sentiment_df.sentiment.count())\n",
    "print(\"\\n Cleaned bitcoin_price count = \", bitcoin_price_df.Price.count())\n",
    "print(\"\\n Cleaned merged df count = \", bitcoin_price_sentiment.Price.count())\n",
    "\n",
    "# bitcoin_price_sentiment\n",
    "analysis = bitcoin_price_sentiment.drop(columns=['Price','text', 'english_translated', 'english_keywords', 'sentiment_scaled'])\n",
    "# analysis = bitcoin_price_sentiment.drop(columns=['Price', 'sentiment_scaled', 'sentiment_cat_scaled'])\n",
    "analysis['Change'] = analysis['Change'].str.replace('%', '').astype(float)\n",
    "display(analysis)\n",
    "# display(analysis.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean of bitcoin price change per sentiment\n",
      "  sentiment_nature    Change  sentiment  sentiment_cat\n",
      "0         negative  0.329683  -0.194557      23.746032\n",
      "1          neutral  0.016255   0.000000      50.000000\n",
      "2         positive  0.392209   0.334011      72.732558\n",
      "\n",
      "Stddev of bitcoin price change per sentiment\n",
      "  sentiment_nature    Change\n",
      "0         negative  3.806280\n",
      "1          neutral  4.145568\n",
      "2         positive  5.089519\n"
     ]
    }
   ],
   "source": [
    "grouped_sentiment_price_change = analysis.groupby(['sentiment_nature'], as_index=False)\n",
    "mean_bitcoin_price_change = grouped_sentiment_price_change.mean()\n",
    "print(\"\\nMean of bitcoin price change per sentiment\")\n",
    "print(mean_bitcoin_price_change)\n",
    "\n",
    "\n",
    "stddev_bitcoin_price_change = grouped_sentiment_price_change['Change'].apply(lambda x: x.std())\n",
    "print(\"\\nStddev of bitcoin price change per sentiment\")\n",
    "print(stddev_bitcoin_price_change)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sentiment_nature', ylabel='Change'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "plt.figure(figsize=(10, 10))\n",
    "sns.violinplot(x=analysis.sentiment_nature, y=analysis.Change, data=analysis, height=8)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "sns.boxplot(data=analysis, x=analysis.sentiment_nature, y=analysis.Change)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 8))\n",
    "sns.scatterplot(x=analysis.index, y=analysis['Change'], data=analysis, hue=analysis['sentiment_nature']);\n",
    "analysis[['Change']].plot(figsize=(20, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion : \n",
    " - Positive sentiments - “happy” reads as a positive Tweet. However, in our case it could be the words “buy” or “long” or “hold,” none of which would read positive using normal sentiment analysis.\n",
    " - Also with cryptocurrency it’s hard to generalize anything.\n"
   ]
  }
 ],
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    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "toc": {
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   "nav_menu": {},
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   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "246px"
   },
   "toc_section_display": true,
   "toc_window_display": true
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