fireant_EDA user-upload.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fireant có total 230k comment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 229676 entries, 0 to 229675\n",
      "Data columns (total 9 columns):\n",
      " #   Column           Non-Null Count   Dtype \n",
      "---  ------           --------------   ----- \n",
      " 0   originalContent  229676 non-null  object\n",
      " 1   date             229676 non-null  object\n",
      " 2   link             10046 non-null   object\n",
      " 3   sentiment        229676 non-null  int64 \n",
      " 4   totalLikes       229676 non-null  int64 \n",
      " 5   totalReplies     229676 non-null  int64 \n",
      " 6   totalShares      229676 non-null  int64 \n",
      " 7   taggedSymbols    229676 non-null  object\n",
      " 8   user.name        229676 non-null  object\n",
      "dtypes: int64(4), object(5)\n",
      "memory usage: 15.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fireant có 15863 user (file sort cmt từ cao xuống thấp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>user.name</th>\n",
       "      <th>originalContent</th>\n",
       "      <th>date</th>\n",
       "      <th>link</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>totalLikes</th>\n",
       "      <th>totalReplies</th>\n",
       "      <th>totalShares</th>\n",
       "      <th>taggedSymbols</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Luu Quan An</td>\n",
       "      <td>1836</td>\n",
       "      <td>1836</td>\n",
       "      <td>1</td>\n",
       "      <td>1836</td>\n",
       "      <td>1836</td>\n",
       "      <td>1836</td>\n",
       "      <td>1836</td>\n",
       "      <td>1836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ẩn Danh</td>\n",
       "      <td>799</td>\n",
       "      <td>799</td>\n",
       "      <td>19</td>\n",
       "      <td>799</td>\n",
       "      <td>799</td>\n",
       "      <td>799</td>\n",
       "      <td>799</td>\n",
       "      <td>799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ông già Nô-en</td>\n",
       "      <td>776</td>\n",
       "      <td>776</td>\n",
       "      <td>239</td>\n",
       "      <td>776</td>\n",
       "      <td>776</td>\n",
       "      <td>776</td>\n",
       "      <td>776</td>\n",
       "      <td>776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chương Nguyễn</td>\n",
       "      <td>769</td>\n",
       "      <td>769</td>\n",
       "      <td>1</td>\n",
       "      <td>769</td>\n",
       "      <td>769</td>\n",
       "      <td>769</td>\n",
       "      <td>769</td>\n",
       "      <td>769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Đỗ Minh Ý</td>\n",
       "      <td>741</td>\n",
       "      <td>741</td>\n",
       "      <td>2</td>\n",
       "      <td>741</td>\n",
       "      <td>741</td>\n",
       "      <td>741</td>\n",
       "      <td>741</td>\n",
       "      <td>741</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>15858</th>\n",
       "      <td>Tình Anh Hai Lúa</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15859</th>\n",
       "      <td>Hailua035</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15860</th>\n",
       "      <td>Martin Nguyen</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15861</th>\n",
       "      <td>Tèo Trần</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15862</th>\n",
       "      <td>Nhan Le</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15863 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              user.name  originalContent  date  link  sentiment  totalLikes  \\\n",
       "0           Luu Quan An             1836  1836     1       1836        1836   \n",
       "1               Ẩn Danh              799   799    19        799         799   \n",
       "2         Ông già Nô-en              776   776   239        776         776   \n",
       "3         Chương Nguyễn              769   769     1        769         769   \n",
       "4             Đỗ Minh Ý              741   741     2        741         741   \n",
       "...                 ...              ...   ...   ...        ...         ...   \n",
       "15858  Tình Anh Hai Lúa                1     1     0          1           1   \n",
       "15859         Hailua035                1     1     0          1           1   \n",
       "15860     Martin Nguyen                1     1     0          1           1   \n",
       "15861          Tèo Trần                1     1     0          1           1   \n",
       "15862           Nhan Le                1     1     0          1           1   \n",
       "\n",
       "       totalReplies  totalShares  taggedSymbols  \n",
       "0              1836         1836           1836  \n",
       "1               799          799            799  \n",
       "2               776          776            776  \n",
       "3               769          769            769  \n",
       "4               741          741            741  \n",
       "...             ...          ...            ...  \n",
       "15858             1            1              1  \n",
       "15859             1            1              1  \n",
       "15860             1            1              1  \n",
       "15861             1            1              1  \n",
       "15862             1            1              1  \n",
       "\n",
       "[15863 rows x 9 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = df.groupby('user.name').count().sort_values(by='originalContent', ascending=False)\n",
    "b = b.reset_index()\n",
    "b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fireant có 25k row đã gắn nhãn, 200k row chưa gắn nhãn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21fd1824d48>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = df.groupby('sentiment').count().sort_values(by='originalContent', ascending=False)\n",
    "a = a.reset_index()\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize = (5,5))\n",
    "sns.barplot(x=\"sentiment\", y=\"originalContent\",data=a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### cộng đồng Fireant hoạt động mạnh từ 2020 (số lượng cmt tăng từ gần như 0 lên 3000 cmt / ngày)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21fd0f2b508>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1 = df\n",
    "df1['date'] = pd.to_datetime(df1['date'])\n",
    "df1 = df1.groupby('date').count().sort_values(by='date', ascending=False)\n",
    "df1 = df1.reset_index()\n",
    "df1['số lượng cmt theo thời gian'] = df1['originalContent']\n",
    "df1 = df1.drop(['originalContent','link','sentiment','totalLikes','totalReplies','totalShares','taggedSymbols','user.name'],axis = 1)\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize = (12,7))\n",
    "sns.lineplot(x=\"date\", y=\"số lượng cmt theo thời gian\",data=df1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 20 KOL trên Fireant (tính bằng cách đếm total like của từng user)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>totalLikes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user.name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tùng Nguyễn</th>\n",
       "      <td>10838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mắt Diều Hâu</th>\n",
       "      <td>10725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F3️⃣1️⃣9️⃣</th>\n",
       "      <td>9067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trangnt2</th>\n",
       "      <td>8210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OMNIS</th>\n",
       "      <td>7470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>George Soros - GSFC</th>\n",
       "      <td>6628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Swing Trader</th>\n",
       "      <td>5474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luu Quan An</th>\n",
       "      <td>4569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deal Cafe</th>\n",
       "      <td>4082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ẩn Danh</th>\n",
       "      <td>3902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CK_TS</th>\n",
       "      <td>3771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VuQuy</th>\n",
       "      <td>3615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elon Tô</th>\n",
       "      <td>2827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>( SĂN CỔ ) LƯỚT SÓNG</th>\n",
       "      <td>2633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hanna</th>\n",
       "      <td>2325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kazan</th>\n",
       "      <td>2314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F0 Lạc Quan</th>\n",
       "      <td>2267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Legendary Bear</th>\n",
       "      <td>2178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thiên tài</th>\n",
       "      <td>2100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Real Guy</th>\n",
       "      <td>2075</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      totalLikes\n",
       "user.name                       \n",
       "Tùng Nguyễn                10838\n",
       "Mắt Diều Hâu               10725\n",
       "F3️⃣1️⃣9️⃣                  9067\n",
       "Trangnt2                    8210\n",
       "OMNIS                       7470\n",
       "George Soros - GSFC         6628\n",
       "Swing Trader                5474\n",
       "Luu Quan An                 4569\n",
       "Deal Cafe                   4082\n",
       "Ẩn Danh                     3902\n",
       "CK_TS                       3771\n",
       "VuQuy                       3615\n",
       "Elon Tô                     2827\n",
       "( SĂN CỔ ) LƯỚT SÓNG        2633\n",
       "Hanna                       2325\n",
       "Kazan                       2314\n",
       "F0 Lạc Quan                 2267\n",
       "Legendary Bear              2178\n",
       "Thiên tài                   2100\n",
       "Real Guy                    2075"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df\n",
    "df2 = df2.drop(['originalContent','date','link','sentiment','totalReplies','totalShares','taggedSymbols'],axis = 1)\n",
    "df2.groupby('user.name').sum().sort_values(by='totalLikes', ascending=False).head(20)"
   ]
  }
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