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<!DOCTYPE html>
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<body>
<h1>Demo of Using twitter-hashtag-analytics to Analyze Tweets</h1>
<p>Building on <a href="https://github.com/benmarwick/AAA2011-Tweets">Ben Marwick</a>, <a href="http://mashe.hawksey.info/2012/01/tags-r/">Martin Hawksey</a> and <a href="http://blog.ouseful.info/2012/01/21/a-quick-view-over-a-mashe-google-spreadsheet-twitter-archive-of-ukgc2012-tweets/">Tony Hirst</a>'s work on analyzing tweets with R, I started an R project for tweet analysis, namely <a href="https://github.com/dirkchen/twitter-hashtag-analytics">twitter-hashtag-analytics</a>. This project is hosted on Github and welcomes anyone who's interested to contribute. It is my very first attempt to write a package in R, so I admit the capabilities of it is still limited and its structure may be not properly planned. Any advice will be highly appreciated.</p>
<p>This demo, drafted with <a href="http://yihui.name/knitr/">knitr</a>, aims to show the functionality of <a href="https://github.com/dirkchen/twitter-hashtag-analytics">twitter-hashtag-analytics</a> and also available on Github. It will evlove along with this project</p>
<h2>Data Preparation</h2>
<p>Before starting to analyze tweets, we will first load a few source files (libraries) in this project.</p>
<pre><code class="r"># check working directory
getwd()
# note that Knitr automatically sets wd to where the Rmd file is. so if
# you wish to run code line-by-line, you should setwd mannually.
# setwd('/home/bodong/src/r/twitter-analytics/twitter-hashtag-analytics')
# load source files
source("get_tweets.R")
source("munge_tweets.R")
source("utilities.R")
</code></pre>
<p>Then we can retrieve a Twitter hashtag dataset by searching through Twitter API. Two other methods of retriving tweets implemented in this project so far include <strong>retriving from Google Spreadsheet archives</strong> (see <a href="http://mashe.hawksey.info/2013/02/twitter-archive-tagsv5/">here</a>) and <strong>reading directly from a CSV file</strong>.</p>
<pre><code class="r"># get tweets by search
# this function is defined in get_tweets.R
df <- GetTweetsBySearch('#LAK13')
# save or load data (so you can reuse data rather than search all the time)
save(df, file="./data/df.Rda")
# load("./data/df.Rda")
</code></pre>
<p>This dataset contains 101 tweets posted by 49 unique Twitter users between 2013-02-20 and 2013-02-26.</p>
<p>Because tweet information retrieved through twitteR is kind of limited (see its <a href="http://cran.r-project.org/web/packages/twitteR/index.html">reference manual</a>, p. 11), we need to extract user information, such as <code>reply_to_user</code> and <code>retweet_from_user</code>, mannually from each tweet. At the same time, the names of metadata in twitteR are quite different from those used in the official Twitter API, the following <code>PreprocessTweets</code> function in <code>munge_tweets.R</code> also renames some attributes of tweets. Moreover, the <code>PreprocessTweets</code> function also trims urls in tweets and put them in a new column named <code>links</code>.</p>
<pre><code class="r"># preprocessing
df <- PreprocessTweets(df)
# structure of df
str(df)
</code></pre>
<pre><code>## 'data.frame': 101 obs. of 14 variables:
## $ text : chr "Professor in Learning Analytics - Milton Keynes - OPEN UNIVERSITY #jobs Anyone interested from #LAK13" "RT @sbskmi: #LearningAnalytics tutorials + practicals in #lak13 open course: Tableau, R + Evidence Hub " "Demo of Using twitter-hashtag-analytics Package to Analyze Tweets from #LAK13 " "Introducing Drake, a kind of 'make for data' from @factual: #lak13" ...
## $ favorited : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ replyToSN : logi NA NA NA NA NA NA ...
## $ created_at : POSIXct, format: "2013-02-26 20:32:29" "2013-02-26 20:22:12" ...
## $ truncated : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ replyToSID : chr NA NA NA NA ...
## $ id : chr "306502014211354624" "306499425751162880" "306492081793282048" "306487793666891776" ...
## $ replyToUID : logi NA NA NA NA NA NA ...
## $ statusSource: chr "&lt;a href=&quot;http://twitter.com/tweetbutton&quot;&gt;Tweet Button&lt;/a&gt;" "&lt;a href=&quot;http://www.tweetdeck.com&quot;&gt;TweetDeck&lt;/a&gt;" "&lt;a href=&quot;http://publicize.wp.com/&quot;&gt;WordPress.com&lt;/a&gt;" "&lt;a href=&quot;http://www.tweetdeck.com&quot;&gt;TweetDeck&lt;/a&gt;" ...
## $ screen_name : chr "EleniZazani" "sjgknight" "bodongchen" "cteplovs" ...
## $ from_user : chr "EleniZazani" "sjgknight" "bodongchen" "cteplovs" ...
## $ reply_to : chr NA NA NA NA ...
## $ retweet_from: chr NA "sbskmi" NA NA ...
## $ links : chr "http://t.co/GfIVItBc74" "http://t.co/baQ8yNlZqV" "http://t.co/DObfNWwmcs" "http://t.co/H6kJET9w2t" ...
</code></pre>
<h2>Start from Easy Stuff: Count Things</h2>
<h3>Count tweets, retweets (by), and replies (to) for each user</h3>
<p>Regular statuses, retweets, and replies are three main types of tweets we analyze. The <code>GetTweetCountTable</code> function can easily count total tweets sent by a user, times of retweeting by other users, and number of replies a user has received.</p>
<pre><code class="r">require(ggplot2)
require(reshape2)
# Count tables
countTweets <- GetTweetCountTable(df, "from_user")
countRetweets <- GetTweetCountTable(df, "retweet_from")
countReplies <- GetTweetCountTable(df, "reply_to")
# quickly check distribution of tweets per user
qplot(countTweets$count, binwidth = 1, xlab = "Number of Tweets")
</code></pre>
<p><img 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" alt="plot of chunk counttables"/> </p>
<pre><code class="r">
# combine counts into one data frame
counts <- merge(countTweets, countRetweets, by = "user", all.x = TRUE)
counts <- merge(counts, countReplies, by = "user", all.x = TRUE)
colnames(counts) <- c("user", "tweets", "replied_to", "retweeted_by")
counts[is.na(counts)] <- 0
# melt data
counts.melt <- melt(counts, id.vars = c("user"))
# plot (Cleveland dot plot)
ggplot(counts.melt, aes(x = user, y = value, color = variable)) + geom_point() +
coord_flip() + ggtitle("Counts of tweets, retweets, and messages") + xlab("Counts") +
ylab("Users")
</code></pre>
<p><img 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" alt="plot of chunk counttables"/> </p>
<h3>Ratio of retweets to tweets</h3>
<p>To get a sense how received or valued one's tweets were within the community, we can further count the ratio of being retweeted by other users to sent tweets.</p>
<pre><code class="r"># create new column 'ratio'
counts$ratio <- counts$retweeted_by/counts$tweets
# plot ratio for users who have at least one rt
ggplot(counts[counts$retweeted_by > 0, ], aes(x = reorder(user, ratio), y = ratio)) +
geom_point() + coord_flip() + ggtitle("Ratio of retweets to tweets") + xlab("Users") +
ylab("Retweets/Tweets ratio")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk ratio"/> </p>
<h3>Count URLs</h3>
<p>URLs embedded in tweets are important because they usually link to important resources that are of interest to this community.</p>
<pre><code class="r"># count links
countLinks <- GetTweetCountTable(df, "links")
names(countLinks)[1] <- "url"
# check top links
head(countLinks[with(countLinks, order(-count)), ])
</code></pre>
<pre><code>## url count
## 1 https://t.co/1hj1FrD8 11
## 2 https://t.co/8Di8QcKz 7
## 3 https://t.co/EgtjcKoU6a 6
## 4 https://t.co/jscpxQpfNA 4
## 5 https://t.co/LnZsOCNFNs 3
## 6 https://t.co/rD8rtO05XV 3
</code></pre>
<pre><code class="r">
# plot to see distribution of links
ggplot(countLinks[countLinks$count > 1, ], aes(reorder(url, count), count)) +
geom_point() + coord_flip() + xlab("URL") + ylab("Number of messages containing the URL")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk counturls"/> </p>
<h2>Social Network Analysis (SNA)</h2>
<h3>Visualize social networks</h3>
<p>An archived tweet dataset contains <code>retweeting</code> and <code>replying</code> as two main type of links among users. Some studies looks into <code>following</code> relations, which require further queries to Twitter. So in this demo, we focus on <code>retweeting</code> and <code>replying</code> links.</p>
<p>The <code>CreateSNADataFrame</code> function in <code>social_analysis.R</code> provides an easy way to create a data frame containing all edges of the requested social network. With created edges, we can easily create an SNA graph and visualize it with packages like <code>igraph</code> and <code>sna</code>.</p>
<pre><code class="r"># load source file first
source("social_analysis.R")
# create data frame
rt.df <- CreateSNADataFrame(df, from = "from_user", to = "retweet_from", linkNames = "rt")
rp.df <- CreateSNADataFrame(df, from = "from_user", to = "reply_to", linkNames = "rp")
# begin social network analysis plotting
require(igraph)
require(sna)
require(Matrix)
require(SparseM)
# create graph data frame (igraph)
g <- graph.data.frame(rt.df, directed = TRUE)
# plot with igraph (quick and dirty)
plot.igraph(g)
</code></pre>
<p><img 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" alt="plot of chunk sna"/> </p>
<pre><code class="r">
# plot with sna get adjacency matrix
mat <- get.adjacency(g)
# convert to csr matrix provided by SparseM ref:
# http://cos.name/cn/topic/108758
mat.csr <- as.matrix.csr(mat, ncol = ncol(mat))
# plot with sna
gplot(mat.csr)
</code></pre>
<p><img 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" alt="plot of chunk sna"/> </p>
<h3>Basic SNA measures</h3>
<p>We can further compute some basic SNA measures. For instance, density of this network is 0.0312, reciprocity of users in the network is 0.9412, and degree centralization of this network is 0.2405. These measures are calculated as below.</p>
<pre><code class="r"># density
gden(mat.csr)
</code></pre>
<pre><code>## [1] 0.03119
</code></pre>
<pre><code class="r">
# reciprocity
grecip(mat.csr)
</code></pre>
<pre><code>## Mut
## 0.9412
</code></pre>
<pre><code class="r">
# centralization
centralization(mat.csr, sna::degree)
</code></pre>
<pre><code>## [1] 0.2405
</code></pre>
<h3>Community detection</h3>
<p>A regular task in SNA is to identify communities in a network. We can do it through the <code>walktrap.community</code> function in <code>igraph</code> package.</p>
<pre><code class="r">g.wc <- walktrap.community(g, steps = 1000, modularity = TRUE)
# number of communities
length(g.wc)
</code></pre>
<pre><code>## [1] 4
</code></pre>
<pre><code class="r"># sizes of communities
sizes(g.wc)
</code></pre>
<pre><code>## Community sizes
## 1 2 3 4
## 5 25 2 2
</code></pre>
<pre><code class="r"># plot
plot(as.dendrogram(g.wc))
</code></pre>
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" alt="plot of chunk detectcommunity"/> </p>
<p>We have detected 4 communities in this network. The largest community contains 51.02% of all users in this dataset.</p>
<h3>Univariate Conditional Uniform Graph Tests</h3>
<p>In network analysis, people do types of tests to check whether some aspects of a network are <em>unusual</em>. We can do such tests, namely <em>conditional uniform graph tests</em>, through the <code>cug.test</code> function in the <code>sna</code> package. Further information about these tests can be found <a href="http://artax.karlin.mff.cuni.cz/r-help/library/sna/html/cug.test.html">here</a>.</p>
<pre><code class="r"># density
cug.gden <- cug.test(mat.csr, gden)
plot(cug.gden)
</code></pre>
<p><img 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" alt="plot of chunk cug"/> </p>
<pre><code class="r">range(cug.gden$rep.stat)
</code></pre>
<pre><code>## [1] 0.4510 0.5419
</code></pre>
<pre><code class="r">
# reciprocity
cug.recip <- cug.test(mat.csr, grecip)
plot(cug.recip)
</code></pre>
<p><img 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" alt="plot of chunk cug"/> </p>
<pre><code class="r">range(cug.recip$rep.stat)
</code></pre>
<pre><code>## [1] 0.4332 0.5615
</code></pre>
<pre><code class="r">
# transistivity
cug.gtrans <- cug.test(mat.csr, gtrans)
plot(cug.gtrans)
</code></pre>
<p><img 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" alt="plot of chunk cug"/> </p>
<pre><code class="r">range(cug.gtrans$rep.stat)
</code></pre>
<pre><code>## [1] 0.4485 0.5477
</code></pre>
<pre><code class="r">
# centralisation
cug.cent <- cug.test(mat.csr, centralization, FUN.arg = list(FUN = degree))
plot(cug.cent)
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk cug"/> </p>
<pre><code class="r">range(cug.cent$rep.stat)
</code></pre>
<pre><code>## [1] 0.0625 0.2585
</code></pre>
<h2>Semantic Analysis</h2>
<h3>Words</h3>
<p>Firstly, make a word cloud.</p>
<pre><code class="r"># load source file first
source("semantic_analysis.R")
# construct corpus, with regular preprocessing performed
corpus <- ConstructCorpus(df$text, removeTags = TRUE, removeUsers = TRUE)
# make a word cloud
MakeWordCloud(corpus)
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk wordcloud"/> </p>
<p>This task first uses <code>ConstructCorpus</code> in <code>semantic_analysis.R</code> to create a text corpus, and then uses <code>MakeWordCloud</code> to make a word cloud. Please note that <code>ConstructCorpus</code> provides a number of options such as whether to remove hashtags (#tag) or users (@user) embedded in tweets.</p>
<p>Next we are going to create a term-document matrix for some quick similarity computation.</p>
<pre><code class="r"># create a term document matrix only keep tokens longer than three
# characters
td.mat <- TermDocumentMatrix(corpus, control = list(minWordLength = 3))
# have a quick look
td.mat
</code></pre>
<pre><code>## A term-document matrix (272 terms, 101 documents)
##
## Non-/sparse entries: 704/26768
## Sparsity : 97%
## Maximal term length: 23
## Weighting : term frequency (tf)
</code></pre>
<pre><code class="r">
# frequent words
findFreqTerms(td.mat, lowfreq = 10)
</code></pre>
<pre><code>## [1] "activity" "analytics" "analyzing" "canvas" "capturing"
## [6] "course" "data" "discussion" "evidence" "feedback"
## [11] "fritz" "john" "join" "learning" "min"
## [16] "network" "peer" "recipes" "scale" "tools"
## [21] "using"
</code></pre>
<pre><code class="r">
# find related words of a word
findAssocs(td.mat, "learning", 0.5)
</code></pre>
<pre><code>## min analytics feedback fritz peer scale join
## 0.78 0.73 0.64 0.64 0.64 0.64 0.60
## using
## 0.54
</code></pre>
<p>For more advanced similarity computation among documents and terms, I am considering adding Latent Semantic Analysis (LSA) capability into this package in the future.</p>
<h3>Topic modelling with Latent Dirichlet Allocation (LDA)</h3>
<p>With the sparse term-document matrix created above, we can use the <code>TrainLDAModel</code> function in <code>semantic_analysis.R</code> to train a LDA model. (Note: I don't understand all of steps in the code in <code>TrainLDAModel</code> refactored from <a href="https://github.com/benmarwick/AAA2011-Tweets">Ben Marwick's repo</a>. So please help to check it if you understand LDA.) This step may take a while depending on the size of the dataset.</p>
<pre><code class="r"># timing start
ptm <- proc.time()
# generate a LDA model
lda <- TrainLDAModel(td.mat)
# time used
proc.time() - ptm
</code></pre>
<pre><code>## user system elapsed
## 122.000 0.012 122.561
</code></pre>
<p>ThiS LDA model contains 24 topics. We can check keywords in each topic, get relevant topics of each tweet, and compute similarity scores among tweets based on topics they are related to.</p>
<pre><code class="r"># get keywords for each topic
lda_terms <- get_terms(lda, 5)
# look at the first 5 topics
lda_terms[, 1:5]
</code></pre>
<pre><code>## Topic 1 Topic 2 Topic 3 Topic 4 Topic 5
## [1,] "contribution" "session" "john" "assignment" "data"
## [2,] "dont" "data" "research" "data" "analysis"
## [3,] "evidence" "education" "session" "expensive" "pandas"
## [4,] "maybe" "matters" "available" "human" "using"
## [5,] "meaningful" "owns" "python" "mining" "available"
</code></pre>
<pre><code class="r">
# gets topic numbers per document
lda_topics <- get_topics(lda, 5)
# look at the first 10 documents
lda_topics[, 1:10]
</code></pre>
<pre><code>## 1 2 3 4 5 6 7 8 9 10
## [1,] 20 16 14 11 5 2 16 16 16 8
## [2,] 24 1 5 17 1 10 1 1 2 2
## [3,] 19 2 1 22 3 1 2 2 3 5
## [4,] 1 3 2 1 6 3 3 3 5 4
## [5,] 2 5 3 2 7 4 5 5 6 1
</code></pre>
<pre><code class="r">
# compute similarity between two documents
CosineSimilarity(lda_topics[, 1], lda_topics[, 10])
</code></pre>
<pre><code>## [,1]
## [1,] 0.8042
</code></pre>
<pre><code class="r">
# computer a similarity matrix of documents
sim.mat <- sapply(1:ncol(lda_topics), function(i) {
sapply(1:ncol(lda_topics), function(j) CosineSimilarity(lda_topics[, i],
lda_topics[, j]))
})
# find most relevant tweets for a tweet
index <- 1
ids <- which(sim.mat[, index] > quantile(sim.mat[, index], 0.9))
sim.doc.df <- data.frame(id = ids, sim = sim.mat[, index][ids])
sim.doc.df <- sim.doc.df[with(sim.doc.df, order(-sim)), ]
# indices of most relevant tweets
head(sim.doc.df$id)
</code></pre>
<pre><code>## [1] 1 63 73 4 50 58
</code></pre>
<h3>Sentiment Analysis</h3>
<p>This project implements three methods (with one method that depends on <em>ViralHeat</em> not working) of analyzing sentiment of tweets. Let's try function <code>ScoreSentiment</code> in <code>sentiment_analysis.R</code> implemented based on <a href="http://jeffreybreen.wordpress.com/2011/07/04/twitter-text-mining-r-slides/">this post</a>.</p>
<pre><code class="r"># compute sentiment scores for all tweets
scores <- ScoreSentiment(df$text, .progress = "text")
# plot scores
ggplot(scores, aes(x = score)) + geom_histogram(binwidth = 1) + xlab("Sentiment score") +
ylab("Frequency") + ggtitle("Sentiment Analysis of Tweets")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk sentiment"/> </p>
<pre><code class="r">
scores <- scores[with(scores, order(-score)), ]
# check happy tweets
as.character(head(scores$text, 3))
# check unhappy tweets
as.character(tail(scores$text, 3))
# check sentiment scores of tweets containing certain words create subset
# based on tweets with certain words, e.g., learning
scores.sub <- subset(scores, regexpr("learning", scores$text) > 0)
# plot histogram for this token
ggplot(scores.sub, aes(x = score)) + geom_histogram(binwidth = 1) + xlab("Sentiment score for the token 'learning'") +
ylab("Frequency")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk sentiment"/> </p>
<p>Sentiment analysis with the <code>sentiment</code> package.</p>
<pre><code class="r">scores2 <- ScoreSentiment2(df$text)
# plot scores. scale_x_log10 is used because the score is based on log
# likelihood
ggplot(scores2, aes(x = score)) + geom_histogram() + xlab("Sentiment score") +
ylab("Frequency") + ggtitle("Sentiment Analysis of Tweets") + scale_x_log10()
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk sentiment2"/> </p>
<pre><code class="r">
# plot emotion
qplot(scores2$emotion)
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk sentiment2"/> </p>
<pre><code class="r">
# plot most likely sentiment category
qplot(scores2$best_fit)
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk sentiment2"/> </p>
<p>We can further check whether these two scores are correlated.</p>
<pre><code class="r"># put them into one data frame
scores3 <- data.frame(score1 = scores$score, score2 = scores2$score)
# scatterplot with regression line
ggplot(scores3, aes(x = score1, y = score2)) + geom_point() + stat_smooth(method = "lm") +
xlab("Score by counting words") + ylab("Score from sentiment package")
</code></pre>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfgAAAH4CAIAAAApSmgoAAAACXBIWXMAAAsSAAALEgHS3X78AAAgAElEQVR4nO3de3gUVZ438FPd1dX3G0mgQ24EzYCAGEEYUJHLCophJRJGhEED7IA6OsOsiuzOOyOOrsPOI6OzoI7uKl6AGQdvLCi4KCg3uYlE4YHEkCvpALn1Jenu9LXePwraGAKpdKrS3Sffz+Pj0+mu7vp1inz79Kk65zA8zxMAAKCXIt4FAACAvBD0AACUQ9ADAFAOQQ8AQDkEPQAA5RD0AACUQ9ADAFAOQQ8AQDkEPQAA5RD0AACUQ9ADAFAOQQ8AQDkEPQAA5dh47djn87W3t8f2XLVaHYlEgsGgtCXFBcMw1EwgynEcISQQCMS7EAnQdFxUKpVCofD7/fEuRBoKhSISicS7CgmwLMuybMwxSAixWq1i9xXzPjr56quvvv/++2AwOHfuXIvFsn379qamJpZlCwsL9Xr95duHQiGfzxfbvtRqdTgcjvnpCYVlWZ7nw+FwvAuRgEqlIoTQcVyUSiXDMKFQKN6FSECpVCoUCjqOC8MwLMvS0cjTarUcx/XmuIgPemm6bs6dO/f9998XFxdPmjRp79691dXVXq+3uLh4xIgRBw8elGQXAAAQG2mCvry8fOTIkQzDDBs2bPr06bW1tZmZmYSQzMzMs2fPSrILAACIjTRdNx6Px+FwvPPOOwzDTJ8+3ev1Dhw4kBBiNpu9Xm90s02bNtXV1Qm3J06ceOutt8a2O4ZhtFqt0WjsfeVxR1NfMMMwhBCdThfvQqRBzaERjotGo4l3IdKg7LjYbLbYnt6jPh9pgp7juFAotHDhwvr6+q1bt+bl5TmdTkKIy+XSarXRze6+++5op2c4HG5sbIxtdyaTKRQKdfwISV409dEbDAZCSFtbW7wLkQBNffQ6nY5lWbfbHe9CJEBTH71Go9FqtQ6HI7an8zzf5enPLkkT9FlZWVVVVQqFQqvV8jyfk5PzzTffEELq6+uzs7Ojm3Vsg7e2tra2tsa2O57nI5EIHeEoNE/oeC9CO4uO90IIYRiGjvfC8zw1/8YYhqHmuEQikT47LtIEfV5eXkVFxeuvvx6JRO66667MzMyysrJNmzYpFIrZs2dLsgsAAIiNNEHPMMzMmTM73tPpRwAAiBeMjAUAoByCHgCAcgh6AADKIegBACiHoAcAoByCHgCAcgh6AADKIegBACiHoAcAoByCHgCAcnFbShAok5GREb1tt9vjWAkAdIIWPQAA5RD0AACUQ9CDxNBvA5BoEPQgDbvd3pvFZABAPgh6AADKIegBACiHoAcAoByCHgCAcgh6AADKIegBACiXfFMgYKg9AECPoEUPAEA5BD0AAOWSL+ij3TXotwEAECP5gp4Q4vF4nE5nvKsAAEgOSRn0AAAgHoIeAIByCHoAAMoh6AEAKIegBwCgXFIGvV6vt1gs8a4CACA5JF/QR6dA6DgXAgAAXEnyBT0AAPQIgh4AgHLJN3ul3W63WCzBYNDj8cS7FgCAJIAWPQAA5RD0AACUQ9ADAFAOQQ8AQDkEPQAA5RD0AACUQ9ADAFAOQQ8AQDkEPQAA5RD0AACUQ9ADAFAOQQ8AQDkEPQAA5RD0AACUQ9ADAFAOQQ8AQDkEPQAA5RD0AACUQ9ADAFAOQQ8AQDkEPQAA5RD0AACUQ9ADAFAOQQ8AQDkEPQAA5RD0AACUQ9ADAFAOQQ8AQDk2jvtWq9WxPVGpVPI8H/PTE4pCoeB5nmXjeSCkolQqSS8Oa0JhGIZhGOEdJTulUqlUKuk4LoQQpVKpUNDQQlWpVAqFIubjEg6HxW8cz3zx+/2xPVGr1YZCoZifnlBYluV5vkfHLGEJ/2TpOC5KpZJhmFAoFO9CJKBSqRiGoeO4MAzDsmwwGIx3IRJQKBQqlapvjgsNH4wAAHAVCHoAAMoh6AEAKIegBwCgHIIeAIByCHoAAMoh6AEAKIegBwCgHIIeAIByCHoAAMoh6AEAKIegBwCgHIIeAIByCHoAAMoh6AEAKIegBwCgHIIeAIByCHoAAMoh6AEAKIegBwCgHIIeAIByCHoAAMoh6AEAKIegBwCgHIIeAIByCHoAAMoh6AEAKIegBwCgHIIeAIByCHoAAMoh6AEAKIegBwCgHIIeAIByCHoAAMoh6AEAKIegBwCgHIIeAIByCHoAAMoh6AEAKIegBwCgHIIeAIByCHoAAMoh6AEAKIegBwCgHIIeAIByCHoAAMoh6AEAKIegBwCgHIIeAIByCHoAAMoh6AEAKIegBwCgHIIeAIByCHoAAMoh6AEAKIegBwCgHIIeAIByCHoAAMoh6AEAKIegBwCgHIIeAIByCHoAAMoh6AEAKJeUQa/X6+NdAgBA0pAy6Nvb21988UVCCM/zn3zyydtvv71p0yaPxyPhLgghGRkZhBCLxSLcAACAq5My6L/44guv10sIqa6u9nq9xcXFI0aMOHjwoIS7AACAnmKleiG73e73+81mMyGktrY2MzOTEJKZmVlSUhLd5rvvvnO73cLtQYMGDRo0qJc7NRgMvXyFuFMoFISQSCQS70IkoFKpCBUHhdB1XDiOUyqVdBwXhmEUCkU4HI53IRJQqVS9OS7BYFD8xtIEfSQS2bVrV1FR0ZtvvkkI8Xq9AwcOJISYzWahjS9obW11OBzCbYvFwrKx7N3j8Qh99JJ3CsUFwzDkUqwkO+FdxHZYEw1lx4VhGDqOC7n0duJdhQR6eVx69GknzbE/cuTIyJEjo+dINRqN0+kkhLhcLq1WG93slltuid5ubW0VtomBx+MJBoMxPz2hsCzL8zwdLRSTyUQIiX5pS2pKpZJhmFAoFO9CJGAwGFiWpePvRUjGHjVmE5ZWq9XpdL05LlarVeSWPQv6cDjc0NBgs9k6faKeO3eura3t9OnTLpdr06ZNEydO/Oabbwgh9fX12dnZPdoFAABIS+w3U7vdPnXqVJPJNGLEiGPHjk2aNKmqqir66D333HP//fcvXLjQbDb//Oc/z83N1ev1mzZtOnXq1M033yxP5QAAIIrYFv3ixYtHjRq1Y8eO4cOH5+fnT5gwYenSpZ9//nmnzR599FFCCMMwM2fOlLhSAACIidig379//+bNmzUaDSGEZdmVK1fm5OTIWRgAAEhDbNdNXl7e/v37oz8ePnx46NCh8pQEAABSEtuiX7t2bVFR0ZQpU1paWoqKivbt27dx40ZZK7sK4fIeu90erwIAAJKI2KCfPHlyWVnZtm3b8vPz09PTX375ZZvNJmtlVxKd+SAjIwNZDwDQrR5cXpmSkrJo0SLZKgEAAFmIDfohQ4Z0usdgMKSlpRUUFPzyl7/U6XQS1wUAABIRG/RPP/30+vXrV6xYkZWVdfbs2TVr1ixYsGDIkCHr1q0rLy9/7bXXZK2yI7vdbrFYgsEgHVMgAADITWzQr1q16tChQ+np6YSQ/Pz8sWPHTp06tays7LbbbsvLy5OzQgAA6JUezNnU8cyn3W5vbW0lhAj/BwCAhCW2Rf+HP/zhrrvuWrRoUU5OTk1NzVtvvfXss8/u2bNn/vz5v/nNb2QtEQAAekNs0C9atGjs2LHvvvvu0aNH09PTd+zYMXbs2NLS0g8++GDixImylggAAL3Rg8srr7/++uuvvz7644YNG+6//34ZSgIAACmJDfrS0tIXX3zR5XIJP/p8vsOHDyPoAQASn9iTsQ888EAgEMjKymptbZ01a9b58+ffeOMNWSu7koyMDL1eb7FY4rJ3uJKMjAyj0Wg0GrFoO0CiEdui/+6773bs2KHRaAoLCxcuXDh9+vS5c+cWFBTIWtzVYQoEAAAxxLbobTbbqVOn9Hq9w+FwOBwmk+nUqVOyVgYAAJIQ26L/7W9/e/vtt5eXl8+cOfP22283GAw33XSTrJV1C835hGK324VOGxwXgETD8DwvctOzZ8+mpaWxLLt58+aWlpb777/fbDbHvOPW1taYB1vRNAUCFgdPTFgcPDHRtzh4c3NzzK8wePBgkVuK7br56quvsrKyNBoNy7ILFiy4+eabZ8+eHWt5AADQd8QG/b333rtt2zZCiN1uX7x48eTJk6dMmSJjXQAAIBGxffQ7d+4sKCh4//33P/zww7lz55aWluIqOgCApCC2RT9ixIi9e/ceO3Zs2bJlb775JlIeACBZdN+iLywsjN5OSUl54YUXTp48qdVqCSFbtmyRsTQAAJBC90H/i1/8ouOPK1askK0YAACQXvddN7NmzZo1a9b06dMvXLhgMplmzZrldDrPnDkzY8aMPqgPAAB6SWwf/fLly//6178KF85fc801f/vb3x5++GE5CwMAAGmIHTCVkpJy9OjRoUOHCj9WVlaOGTOmN0MwMGBKgAFTiQkDphITBkx1JP2AKavV2tjYGP3xwoULqampPa4LAAD6nNjr6J977rmCgoIFCxbk5OTU1dVt2LDhxRdflLUyAACQhNgW/bx58w4cODBw4MDy8nKz2bx7926sOgIAkBR6sJTgsGHDfve730V/xFKCAABJAUsJAgBQLvmWEgQAgB5J4qUEAQBADCwlCABAuSReShAAAMSIfSnBBx54QBgJGRuMjBVgZGxiwsjYxISRsR2JHxnbg8srMzMzz5w5Y7fbx44dm5eXp1CI7fYBAIA46sHllffdd19dXV1OTk5tbe3gwYP/8Y9/DB8+XNbiAACg98S2yhcvXnznnXeeO3fu2LFj9fX1M2fO/Jd/+RdZKwMAAEmIDfrTp08/8cQTKpWKEKJSqR5//PGTJ0/KWRgAAEhDbNDPmjXrgw8+iP74/vvv33HHHfKUBAAAUhLbR6/Vah999NFXXnklNze3qqrqu+++u+uuu+677z7h0XfffVe2CgEAoFfEBv3kyZMnT54saykAACAHsUG/cOFCWesAAACZ4Fp4AADKiQ36y4eh0jEwFQCAet0HfSgUCoVCI0aMCHXgdDrT09P7oD4AAOil7vvoNRoNISQcDgs3oubOnStXUQAAIJ3ug16Y12nGjBk7d+6Uvx4AAJCY2D56pDwAQJISe3nlrl27fv/737e0tHS8s7S0VIaSAABASmKDfsmSJfPnz1+4cCHL9mBmYwAAiDuxqR0MBletWqXVamWtBgAAJCe2j/6xxx5bu3YtHQshAQD0K2Jb9Fu2bCkpKfnjH/9os9kYhhHuRB89AEDiExv0r7/+uqx1AACATMQGvbBqYDgcbmho6NioBwCABCe2j95ut0+dOtVkMo0YMeLYsWOTJk2qqqqStTIAAJBED9aMHTVqVHNzs9lszs/PnzBhwtKlS2WtDAAAJCG262b//v2bN28WprthWXblypU5OTlyFgYAANIQ26LPy8vbv39/9MfDhw8PHTpUnpIAAEBKYlv0a9euLSoqmjJlSktLS1FR0b59+zZu3ChrZQAAIAmG53mRmzY3N2/btq2uri49Pb2goMBms/Vmx62trX6/P7bnGo3GUCjk8/l6U0CCUCgUPM+LPwqJTK/XE1pWpGEYhmGYSCQS70IkoNVqlUplW1tbvAuRAMMwCoWCjpGbarVarVa73e7Ynh4OhwcNGiRy4x5MXBOJRG699Vbhdltb25kzZ6699toeV9dBMBiM7YmRSCQcDsf89ITCsmwkEqEjUIQ/PzqOi1KpZBhGmKM72XEcxzAMHceFYRiWZel4LyzL8jwf83vpUetQbNAvX7587dq1OTk5HSc1O3PmTM9K+7GYm7H8Jb3Ze4IQ3gUd70VAx3vBcUlY+NuPgdigf+ONNw4ePDhhwgRZqwEAAMmJvepm+PDhgwcPlrUUAACQg9gW/bp168aNG3fPPfd0PAf79NNPy1IUAABIR2zQP/HEE1lZWRaLhY7TUwAA/YfYoC8rKysvL7darbJWAwAAkhPbR19cXPzpp5/KWgoAAMhBbNAfOnRo4cKFmZmZwzuQtTIAAJBEDy6vlLUOAACQSc8WHgEAgKTTfdDfdNNNzzzzzFNPPXX5Q19//bUMJQEAgJS6D/pXX301Nzf3z3/+szBlVRQdc1cBAFCv+6DPz88nhDzwwAMVFRXRO9va2rKzs2Oedw0AAPpM90EvrCoVDoeFG1Fz586VqygAAJBO90EvDIWdMWPGzp075a8HAAAkJvY6eqQ8AECSEnt55a5du37/+9+3tLR0vLO0tFSGkgAAQEpig37JkiXz589fuHBhx4VHAAAg8YlN7WAwuGrVKq1WK2s1AAAgObF99I899tjatWvpWJMXAKBfEdui37JlS0lJyR//+EebzcYwjHAn+ugBABKf2KB//fXXZa0DAABk0rNJzcLhcENDQ8dGPQAAJDixffR2u33q1Kkmk2nEiBHHjh2bNGlSVVWVrJUBAIAkxAb94sWLR40a1dzcbDab8/PzJ0yYsHTpUlkrAwAASYjtutm/f//mzZuF6W5Yll25cmVOTo6chQEAgDTEtujz8vL2798f/fHw4cNDhw6VpyQAAJCS2Bb92rVri4qKpkyZ0tLSUlRUtG/fvo0bN8paGQAASEJs0E+ePLmsrGzbtm35+fnp6ekvv/yyzWaTtTIAAJCE2K4bQojD4ViwYMHjjz/u8/m2b98eDAblKwsAAKQiNuifeeaZUaNGud3uNWvWbNiw4b/+67+WL18ua2UAACAJhud5MdulpKTs2rXrhhtuGDx48OHDh0Oh0Pjx45uammLecWtra2tra2zPtVgswWCQjkVrWZbleZ6OSYRMJhMhhI4FJpVKJcMwwqo7yc5gMLAs63Q6412IBBiGYVmWju4ErVar0+mam5tjfoXBgweL3FJsH304HLZYLEePHh00aFB2dvb58+cDgUCs5QEA9Ec1NTXR2xzHjRo1qm/2Kzbo77vvvjvvvDMYDP6///f/qqqq5s+fP336dFkrAwBIXh0zPe7EBv1LL7300UcfEULmzJlTWVn5s5/97MEHH5SzMACA5JBQmd4lsX30kkMfvQB99IkJffSJKe599BJmutB1k1h99AAA/Urit9PFQ9ADQL9GU6BfSQ+CvrGx0eVydbzn2muvlboeAAC59IdM75LYoF++fPnatWtzcnJY9oennDlzRp6qAAB6pd9mepfEBv0bb7xx8ODBCRMmyFoNAEBPIdO71YOlBMWf4QUAkFxNTQ3DMAqFgo6r1PqS2KBft27duHHj7rnnno6TVj799NOyFAUA/Rsa6dISG/RPPPFEVlaWxWKh48piAEgQyPQ+IDboy8rKysvLrVarrNUAAK0Q6HEkNuiLi4s//fTT+fPny1oNAFAAmZ5oxAb9oUOH/vKXv6xYscJgMETvLC0tlacqAEgCCPRk0YPLK2WtAwASGTI9qfXg8kpCSDgcbmxsTE1N7ThsCgCogUCnkti8bmxsfPTRRz/88EO1Wu33++fMmfPSSy+lpaXJWhwAyASB3q+IXTN22bJlOp2uvr6+ra2tvr5eo9E89NBDslYGAJKoqKgoLy+v+bF4FwV9SmyLfteuXTU1NcLllWlpaS+88MLQoUPlLAwAeuZK8a3RaPq4Ekg0YoPeZrMdO3bs9ttvF348fvx4enq6bFUBwBWhPQ49JTboV69ePXfu3MLCwiFDhlRXV2/ZsmX9+vWyVgbQzyHQQSpig76oqOjGG2/cunXr+fPn8/Pzn3rqKXTdAEgCgQ5yExv0o0eP/vvf//6b3/xG1moAKIZAh3gRG/T33nvvmjVrXn31VbVaLWtBIlVUVPj9fuF2Tk5OfIsBiEKaQwISG/Sff/55SUnJ3/72t6ysrOhoqbhMgZCRkUEImTRpEiHkT3/6E7nsTwu5HxfCcRHY7fY4VtI3kiXQV65cGb0t/L1AP9R90Dc0NAwcOHDVqlXJMjyq418gQh96I1nSHODqug/6YcOGnT59uri4+NSpU31QkLTQ2IdudZnmCoWCYZi+LwZADt0H/S9+8YvrrrvO5XJlZmZ2esjpdMpT1dXY7XahlyCG76GX/0kj+qUSPS6J2W/Tb9vmf/rTn4TeG/Tb9GcMz/Nitrvnnns++ugjCXfc2tra2toa23MtFktpaWn0ZKy0+jj6WZbleZ6ONTBNJhMhxO12x2Xv0ka50KKn47hoNBqFQuH1euNdiARoWjOW47hRo0Y1NzfH/Ari1/EWezJW2pRPZGj1J6x+2yoH6CXMNty9LvMF6S8thDiAfKQJ+lAo9PHHH7tcrvb29lmzZg0ePHj79u1NTU0syxYWFur1ekn2klCuEkz4DOgEIQ4QXz0L+nA43NDQYLPZOl2QUFFRwXFccXFxfX399u3b/+mf/snr9RYXFx8/fvzgwYPRqdD6iW5zjYJPgi7fo1arJYT4fL4+LwcArkZs0Nvt9oULFx45coTjuM8+++xf//Vf33nnndzcXOFRk8k0fvx4QohOp2MYpra2VrhEJzMzs6SkRKbSk1fHlFQqlTzPRyKRnr5Ijz4t0KYG6M/EBv3ixYtHjRq1Y8eO4cOH5+fnT5gwYenSpZ9//rnwqDBlsd1u//jjj6dNm3bmzJmBAwcSQsxmc8dz/evXr6+trRVu33bbbdOmTYutaIZhJk2a9Nprr8X2dDq4XC7xG1ssFvkqiXrwwQcJIf38uCQgHJfE9OCDD+7bt0/kdY+X69FlVGIvrxSWl7JYLMI0xU1NTTk5OR6PR3iU5/ndu3fX1tbOnDnTZrN98cUXGo1m4sSJjY2N27ZtW7JkyeUvGPPllZdPgZDUYm7RJxrKhtpTc3klZceFmssrheOyb98+0ouhJ+IvrxS7lGBeXt7+/fujPx4+fLjjNMWnTp1yOBzFxcU2m40QkpOTI5ReX1+fnZ0ttmoAAJCB2K6btWvXFhUVTZkypaWlpaioaN++fRs3bow+WlFRUVdX99///d+EEJPJNH/+/LKysk2bNikUitmzZ8tSOAAAiCO264YQ0tzcvG3btrq6uvT09IKCAqHxHrPejIzNyMiYNGkSBd9DCUVdN+TSt1E6jgs1XTeEruNCTdcNIYTjuJtuuqk3U4aI77oRG/TCwiMjR46MuaZOEnYKhD5GU9DTdHklTUGPKRASU19OgSC2j15YeISObAUA6FeSb+ERAADoEbFB/9JLL8laBwAAyERs0I8aNYoQEg6HGxsbU1NTo416AABIcGL76BsbG+fNm6fRaK699lqtVjtv3rzGxkZZKwMAAEmIDfply5YJg2Pb2trq6+s1Gs1DDz0ka2UAACAJsT0wu3btqqmpsVqthJC0tLQXXnih48hYAABIWGJb9Dab7dixY9Efjx8/LkxkBgAACU5si3716tVz584tLCwUJjXbsmXL+vXrZa3sKiI8cXhUGmVAwcQ48RsAQP/RgykQKisrt27dev78eZvNdvfdd/ey66Y3I2MjSusDq9m2dpVBE7Dog2ZtwKwLmHUBi85v0QctuoBZ59dyyTF2DiNjExNGxiYmjIztSPrFwf1+/xdffDFmzJjbbrtt48aNW7du/eUvf8lxXKwV9soAI7/mgRMeX9DRxjm96pY2zuFRu31cbZPB6eVcXs7tU3FsxKr3m7QBiz5g0QXMusAAQ8CkDVh0AZM2yOCrAAD0G2KDfvny5V9//fUbb7xBCLnmmmvWrl174sQJ4cd4YRWRNFN7mqm9y0e9Adbt5VxelcvHuX3cOYeutN7i9nFOj8rt41hlxKwNpJr8Jm3ArA2YdUGzLpBiaDfrgkZtQMF0+ZIAAElJbNdNSkrK0aNHo901lZWVY8aMcTqdMe84jpOahcIK16XEd3k5l1fV3KYRbjg8agXD69Qhiz6YYmg3aQMWfVD4MEg1+S06v1Ih8VcBdN0kJnTdJCZ03XQkfdeN1WptbGyMBv2FCxdSU1NjKS0BsMpIiqE9xdDFV4FgWOFo49w+zuFVuzwqp5erbjS4vCqnR+32qRQMb9YHLbqARec364IWvd+sDVj0whmCIKugIawBgD5ig/65554rKChYsGBBTk5OXV3dhg0bXnzxRVkriwuVMjLQ3D7Q3MVnAM8zbp9KOAfg8HAuL3e2WX/CY3X7OKdX7Q8qDJqgSRvseELYog9adH6zLqBXh/r+vQAACMQG/bx58/Lz8997773y8nKbzbZ79+7Ro0fLWlmiYRheuLany0f9QaXDwwl9QcKHwdlmndvHOb1cq49TKiIWfcCsDVj1wleBgFnnN+uCVr3faogwBGeGAUBGooKe53mXyzVs2LDf/e53R44cKSkpEXpjIUqtCtssPpul6+7pYFjh9nFNbvWlM8Pa0nqzcGa4tZ1TKnBmGABk1H3Ql5eXFxYWDh06dNu2bUeOHLntttsmTpy4YsWKzZs333HHHX1QIgVUVz4rwBOVw8M62tjomeHKBmO8zgwDAJW6D/rHHnusoKBg9erVhJCnnnrq1VdfXbRo0V//+tc//OEPCPreY5WRVKN/gL6LrwLBsMLh4dzeH84M1zQanB3PDOsCwvkAYbiAURMYYBAGjgVYJc4MA8BF3V9eOWDAgFOnTtlsNq/Xm52dbbfb1Wp1WVnZuHHj3G53zDvGmrGC2C6vFM4MOzxcazvnaOPc7ZzTw7m8KpdX7fRyvoDSoAmZL14dFDDrAlbhDIEhYNYGdLKdGcbllYkJl1cmpsS6vDIcDgv/RPbs2TN+/Hi1Wi3cr9frY64PeunqZ4bDEaatXRUdLNbkVpefN7l9nNvLtbRx/pBSx4U6nhIw6QKphnbhglGNioY/IQDoqPugHz9+/Pr161euXPmXv/ylsLBQuHPTpk3jxo2TuTaIkVIR/RjwXP5opzHDTW51bbNezJlhkzbA4MwwQBLqPuj//Oc/FxQUPPfcc9dff/2SJUva29sLCgq++eabo0eP9kF9IDkdF9JxIZuli4eE7wFOr9rp5dxersXD1Tv0wqABb4DVqUPR/h+rPmDW+a36gEXvH6D3q1U4JQCQuLoP+tGjR1dXV9vt9sGDB7MsGwgElixZ8u6776alpfVBfdCXDJqgQRPMGNBFZ24gpHB6OJePc3rULh/n9HB2h074VGj1qdSqsFUfSDGGLPqgnvP2zSkBABBJ1HP3WqYAABdUSURBVHX0SqUyOztbuM1x3M9//nM5S4JExLFXGTNMWttVLi/nDRkcbVyTW3HmvMnl5Vw+zuFRRyLEqg+YhMFil8YMC4PFTNogVhQA6ANiR8YCXAnDEJM2aNIGtdoIueyqm1CY8fhV0ZnjnB5VTZNeODfg8KgVCv7yUwLCiAGrvh2DxQAkgaAHebHKi2eGs1O7PjMcHTDs9nLnndqTdVbh9EA4otBxwY5jxKIfBlZ9O84KAIiHoId40nGh7NTQ5VcHRXjG7VU5Lp0PcHi4umad02t1ejmXR0UYRlhDRphByKL3m7RBqz5g1Aas+oAKg8UAfgxBD4lIwfAWfcCi73qgQKtP5fJxDo8wTIyzt+hPX5xOTuULsFouJCwoJnwGWPUBsy4wQJ9MC0wCSAtBD8nHqA0atcHMAV30BQVCCqeXc/s4p4dz+1ROj7rUpXV51U6PyuXjlAxvNQTMOuEz4OLSksJIMZwZBooh6IEqHBsZaGofeIUFJj1+1u3jHG2cw6t2tHG1TYZvPWqnl3N4uHCEMWkvThw0wBAw64IDDEGjpl2YSQKnBCCpIeihH9GrQ3p1KN3SxUABj591eTmn0Pb3qpta1RUXjMJgMY+f1XLhTovJCDMImbQBkzbY928EoEcQ9ACEXPoMGGy9+BnQcVKzkDCNqI9raeOExWTO15uFsWNun4phiFEjrCsZFD4JLk4npw1YDTgzDAkBQQ/QDVYZSTO1p5narxnU+aEIT9raOZdXJUwa4fJxF1ya8vMmR9uleSO4kFkXsBoCJq1wQjgQnVXUoMGYYegjCHqA2CkYYtIGTNpAVkoXZ4bJZQMFapv0TW0pbi/n8HDtQaWOC3WcPdSkCwhjBdJM7bhACCSEoAeQ0ZUGCpDLFph0elTnnObmNk3HaUQ7zh6KBSYhZska9Ndcc43H03Ubqjdqamokf02ALl1lgUmvnxVmCnJ5VQ4P5/Kqzzl1Do/a7ePa2lktFxamDjXrghad36QVzg8Ls0cEGVwkCpdJ1qCXSU5OjviN8akAMtGpQ7orXB0knBl2+ThHG+fycS4vd8GldXkvLjgc4RmTNmjV+026gFUfEK4WtQ0gVkNIo2jHApP9FoI+dlf/VMDHAMghemaYXHZmmBASCitcPs7pUUXXmq9pMnxVrnF5VU2tKqWC7zRzXHTR+QF6P74KUAxBL5crfQzgAwDkw3bVHSSsGetu9QnTiEZXFqt3aEvrzW4v1+JRh8KMXo0p5KiFoO9rnT4AWJbleb6ysjJe9UA/EZ1G9PIzw6EwI4wPcHg4t5dzeNRnm/VOj1VYfZ4QYtQIa4pF//vh3AA+A5ICgj4hXN78R8Mf+gyr5AcY/AMM/ssfElaVESaPEyYRuuDSlZ9TCSPIPH5WWFxMmDXIqg+YdAGrzm/WBSz6gFETxCLDCQJBn6AQ/ZAIoqvKdHmFaPSUgPAZ4PCoKy4YXd7UTrNJX/wA0PuFueSEDwalAqcE+g6CPml0in7kPsRdl6cEoi4/M1zVYBRuRBcX63KggEkbwFcBaSHokxVyHxLc1T8GrrS4mMPDhcKMTh3qeFHQD1cHGQKKPn4bVEDQU6Jj7iP0IfFdacwwzzNu38VzAMLsofUO7ak6szC3KCHCWvN+oQvILCw6j8Fi3UHQUwiNfUheDBO9OqgL7UGV26dubmWF/p9zTt3peoujjXN61d6A8uKKArpg9HyA5dIscv386iAEPf3Q2AdqaLmQXhMZaOpixrdASNHcpnZeWkmmuU1d2WDsNG+ERRew6INWvf+Hs8TagLEfrCiAoO9f0NgHWnFsJN3iS7f4unzUG2DdXs7lVTW1adxerq5ZX1KT4vZyTi/nC1xlGlG/lqNhNmkEfb+Gxj70EzoupONCNgsZRlydHrrKNKJt7Zyiq2lEk27eCAQ9XITQh/7pKtOI+gKsy8t1nEXu/DmdMH64tV0lDBYzawMm3cVV5s06f3TYcEINFEDQQxeioY/Eh/5My4W0XMjW5TSiEYXLo3J6OZdX7fKpnB51daPB6RkgTCYRDCuMmqBRGxigD1gNfqs+YNH5Bxj8Vn3AqAmwyr7+DEDQw9WgmQ/QJVYRSTH6U4x+Qlovf1T4KuD0qoSpIxwerqrB4PapHB51q4/Ta4ImTWCAMfS7QYy2T8YFIOhBLIQ+gEiXvgp08VA4wji9nNOjdrfrzXpNoOuTxxJD0EMsEPoAsVEq+BSDP8Xg57h2oy69GUEPSSEa+g0NDfGtBAC6hKAHyVx77bWEELfbTdDMB0gkCHqQBa7bAUgcCHqQF3rzAeIOQQ99B818gLhA0EMcoJkP0JcQ9BBnaOYDyC2eQc/EulwYc4m09cQFwzA8z9PxXgQxv5chQ4YINxIh8YV3geOSaGj62xfE/F54vgfzKMQz6FUqVWxPVCgUSqUy5qcnFIVCoVAoenTMEpZSqSS9OKxRwmWagqqqql6+WmxoCnohGYWjk+xoSnmFQsEwTMx/L+FwF5PyX0k8gz4Q6HoRmW6Fw+FQKBTz0xMKy7I8z/fomCUsjUZDenFYu5SRkSHc6ONmvvBHSMdxEf6NhUI0zKvOMIxCoaDjuAgtvL7JMfTRQ3JAVz5AzBD0kGSQ+AA9haCHZIVrNAFEQtADDdDMB7gKBD1QBYkPcDkEPdAJiQ8QhaAHyiHxARD00F8g8aHfQtBDv4PEh/4GQQ/9Fy7QhH4CQQ9ACJr5QDUEPcCPCImvVCqR+EANBD1A14YMGSJMBIbEh2SHoAfoBnp1INkh6AHEQuJDkkLQA/QYEh+SC4IeIHZIfEgKCHoACSDxIZEh6AGkhMSHBISgB5AFEh8SB4IeQF5IfIg7BD1AH0HiQ7wg6AH6GhIf+hiCHiBukPjQNxD0APGHxAdZIegBEggSH+SAoAdIREh8kBCCHiChIfGh9xD0AMkBiQ8xQ9ADJBkkPvQUgh4gWSHxQSQEPUDSQ+LD1SniXQAASCYnJyca+gBRaNED0AYNfOgEQQ9ALSHxDQZDVVVVvGuBeELQA9AvLy/P6XQStPH7KwQ9QD+CXp3+CUEP0B8h8fsVBD1Av4bE7w8Q9ABACBKfagh6APgRJD59EPQA0DUkPjUQ9ADQDSR+ssMUCAAgFqZYSFJo0QNAz6CBn3QQ9AAQIyR+skDQA0BvIfETHIIeACSDxE9MCHoAkB4SP6EkX9BnZGQIN+x2e3wrgU6MRiPBcUk8ZrNZuBGXQ4PETwRJfHllNPEhEUQPB45LQkmcw5FzSbwL6Y+Sr0UPAEkNbfy+l8QtenQRAHQrkf9M0MDvM2jRgzTsdrvJZCKEuN3ueNcCP+JyuViWFVaYSkBo4PeBJA76jIyMRG6tAECPIPHlk8RBDwBUQuJLLomDHs15ALoh8aWSfEFvt9stFkswGPR4PPGuBQD6QjTxa2tr41tJkkq+oO94XTAa9QkFA6ZAbjk5OSzLBoNBtPF7JIkvr4SEggFT0JdwaWaPJF+LHgBAgE58kZI46NFFAAACJP7VJV/Xjd1u93g8CTv6o9+Kfu7iAxjiCDPqdEmWFj3P89u3b29qamJZtrCwUK/Xy7EXSDStra0EI2MhMaCN35EsLfrq6mqv11tcXDxixIiDBw9K/vp6vd5isUj+sgBUMpvN/bmxhTY+kSnoa2trMzMzCSGZmZlnz56V9sVxdQeAePh7ierPiS9L143X6x04cCAhxGw2e73e6P0HDhxoaWkRbufm5g4dOrSXO6KgXc8wDCGE5/l4FyIBlUpFqDgohK7jEkXHoVEoFJFIpDevIPweysvLJaooRgqFgmXZmA+K3+8Xv7EsQa/RaISTpS6XS6vVRu83Go3Rvxy1Wh0KhXq5o96/QtwpFApCSC//4SYIpVJJqDgohK7jEkXBoWEYhuf5cDjc+5fKzc0VblRUVPT+1WIgvJeYD0qP/nHKEvQ5OTnffPMNIaS+vj47Ozt6/+jRo6O3W1tbhXN3PdVxCoS2trbeVxtfLMtK9Q837oRwpOCgEEKUSiXDMBQko91uNxgMwjTFFBwahmGEkbESvuagQYOEG3182pbjuHA43DcHRZagz83NLSsr27Rpk0KhmD17thy7AACQFsUX6sgS9AzDzJw5U45XBgCQG32Jn8QjYwEAZEVN4iffyFgAgD6W7NdlokUPACBK8jbwEfQAAD2TdImPoAcAiFGyJD6CHgCgtxI88RH0AACSSczEx1U3AADSS6gLddCiBwCQS4I08BH0AACyi2/iI+gBAPpONPHPnTvXZztFHz0AQBz0fkEO8RD0AACUQ9ADAFAOQQ8AQDkEPQAA5RD0AACUQ9ADAFAOQQ8AQDkEPQAA5RD0AACUQ9ADAFAOQQ8AQDkEPQAA5RD0AACUQ9ADAFAOQQ8AQDkEPQAA5RD0AACUQ9ADAFAOQQ8AQLmkXBz8xIkTWq02NTU13oVIIBKJxLsEyZSVlTEMY7PZ4l2IBHie53k+3lVIo7q6ur29PTMzM96FSIOaP5lz5845HI7c3Nw+2Ffcgt5oNBqNxtiee+TIEZvNNnjwYGlLgl46deoUwzBjxoyJdyHwIzU1NQ0NDePHj493IfAjLS0tVVVVt9xySx/sC103AACUQ9ADAFAuKfvo09LSzGZzvKuAzgYMGBDvEqALZrOZmvMNNDEYDOnp6X2zLwb/AgAA6IauGwAAyiHoAQAol2R99DzPb9++vampiWXZwsJCvV4f74rgB3v37k1NTR0xYkS8C4EfhEKhjz/+2OVytbe3z5o1KyMjI94VASGEBAKBDz74oL29PRKJzJkzx2q1yrq7JGvRV1dXe73e4uLiESNGHDx4MN7lwEWRSOTNN9/88ssv410IdFZRUcFxXHFx8T//8z/v2LEj3uXARd9+++3gwYMXL148ZsyYQ4cOyb27JGvR19bWCgP8MjMzS0pK4l0OXMQwTHFx8RdffBHvQqAzk8kkDJXS6XQMw8S7HLgoJydHq9USQhiGUavVcu8uyYLe6/UOHDiQEGI2m71eb7zLgYuYS+JdCHQmXMBnt9s//vjjadOmxbscuEjIsX/84x/V1dXLli2Te3dJFvQajcbpdBJCXC6X8HkIAFfB8/zu3btra2tnz55NxzREdPD7/SqVat68eVVVVR9//PH9998v6+6SrI8+JyfHbrcTQurr67Ozs+NdDkCiO3XqlMPhKC4uRsonlL1793777beEEJZlw+Gw3LtLsgFTPM9/+umnLS0tCoVi9uzZOp0u3hXBD3bv3m2z2XDVTULZunVrZWWlRqMhhJhMpgULFsS7IiCEkNbW1o8++igYDEYikVmzZsk9RDbJgh4AAHoqybpuAACgpxD0AACUQ9CD9L777rsZM2aYzeaUlJS77767vLxcjr1s3Lhx4cKFcryyeCzLhkKhr7/++qabbopvJU6n02KxxLcGSFgIepBYOBwuKCgYP3788ePHT548OWzYsHvuuYfuU0G5ubnPPPNMvKsAuCIEPUjMbrfX1dWtXLly6NCh6enp//mf/5mbm+tyuQghmzdvzsvLS0lJefjhh/1+PyHkww8/HDZsmNlsLioqamxsJIScPHlyypQp//Ef/zF69GhCyL59+2688Ua9Xn/nnXcKV9Z25PP57rvvPrPZPGHChJMnTxJClixZ8vzzzwuPrlq16te//nXH7cUUcOjQoQkTJgjbR2+Xlpbeeuuta9asycjIyM3N3b17NyFkxowZ4XD4mmuuOXny5FNPPXWlzQghb7311pAhQ4YMGfL2228PGTKkY0njxo3bvn07IeT555/nOK69vZ0QMm3atHfeeUfM72fdunVZWVlZWVnr168XXjAUCj388MNWqzU1NfXZZ5/t9fEEKvAAkgoGg6NGjbrjjjt27Njh9Xqj95eVlaWkpHz11VcVFRVjx459/fXXKysrzWbzzp07m5ubFy1aNG/ePJ7nT5w4YTabFy1adPLkyaamppSUlK1bt7a0tDzyyCNTpkzpuKMNGzYQQl577bWGhoYnn3zyuuuuC4VCH3zwQXSz0aNH79mzp6cFHDx48Kc//anwlOjt06dP6/X61atXezyeJ598cuLEicIGSqUyGAwePXp07NixV9rsu+++S01NPXz4sN1uv/XWW3Nycjq+iyeffPLf//3feZ6fM2eOwWA4cOBAIBDQ6/V1dXXd/n727t1rtVr37NlTV1c3bdo0YYGRzZs3Dxs2rKqq6ptvvlGr1WfOnJHw4EKSQtCD9Nrb21955ZU777wzJSXljjvuOHLkCM/zzz777K9+9Sthg5KSkj179rz44ovFxcXCPQ0NDSqVKhQKnThxQmjY8jz/1ltvFRUVCRv4fD6dThcKhaJ72bBhw5gxY4TbgUBgwIABp0+fdrvder3e6XRWVFQMGjSo4/YiC7hS0JtMpmAwyPP8iRMnhg0bJmxwedBfvtlvf/vbFStWCNv/7//+b6eg//TTTydNmhSJRLKzsx955JHnn3/+6NGj1113Hc/z3f5+li9f/m//9m/CBgcOHIgG/dChQw8fPhyJRBobG/1+f08PH9AnyaZAgMQXCAR4nn/44YeF7pG///3vkyZN2rdvX11dXV5enrDNDTfcQAjZvn17tB8jLS2N4zihdyIrK0uY5uns2bM7d+6MbsNxXENDQ8ehJbm5ucINlUo1ZMiQ8+fPDx8+/JZbbvnss89qa2vnzJmjVCqjG4svIIrvcGrBZrOxLEsIEf5/JZdvZrfbx40bJ9y+fDj3rbfeWlJScubMmbS0tBkzZrz11lscx91+++2EkPPnz1/993P+/HlhS0LI0KFDhRtz5sxxu93Lli27cOHCI4888vjjj1+lWugn0EcPEnvvvfcKCgqE22q1etGiRRMnTjx+/PigQYPq6uqE+w8ePLhhwwabzVZTUyPcI7Q9U1NTSYeITE9Pnz59enV1dXV1dUVFxWeffdZpHH9VVZVwIxgM1tTUZGVlEUJmzZr1ySefbNmyZe7cuR03Fl9AKBQS7oxuTwgROWXb5Zulp6fX1tYKt8+ePdvpUb1en5+f/9JLL02YMOHmm28+ePDggQMHhPju9vczePDgioqKTr+KysrKadOmlZSUHD58eNu2bW+88YaYsoFy8f5KAbS5cOGCxWJ56qmnvv/++9LS0ldeecVoNH7//ffffvttSkrKoUOHKioqxo8f/8ILL5w5c8ZkMn3++ectLS0PPPDAz372M/7HHSPnzp1LS0v75JNPGhsbn3jiiVtuuaXjjjZs2KBQKP7nf/6nsbFxxYoVN910UyQS4Xm+srLSarUOGjRI6EKJElnAqVOnWJY9fvx4U1PT5MmTo1030ao63lYqlQ6Ho2PXzeWbff3112lpaUePHq2vr588eXKnrhue51etWqXX6zds2MDz/HXXXWc2m51OJ8/z3f5+Dhw4YLVa9+7da7fbb7/9dovFwvP8mjVrfvrTn54/f76ysvKGG254/fXXe39MIdkh6EF633//fUFBwaBBg4xG48SJE3fs2CHc//bbb+fm5ppMpsWLFwt9x++///5PfvITo9FYWFh44cIF/sdBxvP8//3f/40cOVKn002dOrWioqLjXjZs2LBs2bLCwkKj0Thp0qSOZx1Hjhy5dOnSywsTU0AkEvnVr35lMBiuv/7699577+pBf++99xqNxi+//PIqQc/z/Kuvvpqenv6Tn/zk1VdfHTlyZKeq9uzZQwgpLy/neX7p0qXRM71ifj/r1q3LzMzMyMhYv359RkYGz/Otra3C4msDBgx46KGHhJ406Ocw1w1Q6Oabb3766adnzJgR70IIIaS0tPTChQuTJ08mhOzcuXP16tVYoQX6GProgSoej+fLL7+sra2dOnVqvGu5yOFwzJ8/v6GhwefzrVu37q677op3RdDvIOiBKjt37pw/f/7LL7+sUqniXctFEydO/PWvf33jjTfm5eWlp6c/8sgj8a4I+h103QAAUA4tegAAyiHoAQAoh6AHAKAcgh4AgHIIegAAyiHoAQAoh6AHAKDc/weUxV9Hx1PFBQAAAABJRU5ErkJggg==" alt="plot of chunk sentimentcompare"/> </p>
<p>Finally, this project is at its early stage. If you are interested, please fork <a href="https://github.com/dirkchen/twitter-hashtag-analytics">twitter-hashtag-analytics</a> on Github.</p>
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