Social Media Data Mining on 2017 NBA Champ

Sentiment Analysis from Twitter

Sentiment analysis is the computational study of people’s opinions, sentiments, emotions, and attitudes. This fascinating problem is increasingly important in business and society. It offers numerous research challenges but promises insight useful to anyone interested in opinion analysis and social media analysis. Read more at https://www.cs.uic.edu/~liub/. and https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html.

In their KDD-2004 paper, they proposed the Feature-Based Opinion Mining model, which is now also called Aspect-Based Opinion Mining (as the term feature here can confuse with the term feature used in machine learning). The output of such opinion mining is a feature-based opinion summary or aspect-based opinion summary. The commonly known sentiment classification is a sub-task.

Customer Review Datasets (5 products) associated with the paper (Hu and Liu, KDD-2004).(Mingqi Hu(胡岷青).Bing Liu, University of Illinois at chicago) can be obtained from https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html#lexicon.

Positive words and Negative words can also found at: http://ptrckprry.com/course/ssd/data/positive-words.txt. and http://ptrckprry.com/course/ssd/data/negative-words.txt.

An example of application will be A sentiment analysis job about the problems of each major U.S. airline. Twitter data was scraped from February of 2015 and contributors were asked to first classify positive, negative, and neutral tweets, followed by categorizing negative reasons (such as “late flight” or “rude service”). see: https://www.kaggle.com/crowdflower/twitter-airline-sentiment.

Step 1: We will establish what is positive and what is negative:

library(RCurl)

## Loading required package: bitops

myfile <- getURL('http://ptrckprry.com/course/ssd/data/positive-words.txt', ssl.verifyhost=FALSE, ssl.verifypeer=FALSE)
mydat <- read.csv(textConnection(myfile), header=T)
df_positive <- tail(mydat,-47)
colnames(df_positive) <- "positive"
head(df_positive)

##      positive
## 48         a+
## 49     abound
## 50    abounds
## 51  abundance
## 52   abundant
## 53 accessable

myfile <- getURL('http://ptrckprry.com/course/ssd/data/negative-words.txt', ssl.verifyhost=FALSE, ssl.verifypeer=FALSE)
mydat <- read.csv(textConnection(myfile), header=T)
df_negative <- tail(mydat,-47)
colnames(df_negative) <- "negative"
head(df_negative)

##      negative
## 48    2-faced
## 49    2-faces
## 50   abnormal
## 51    abolish
## 52 abominable
## 53 abominably

library(tm)

## Loading required package: NLP

library(wordcloud)

## Warning: package 'wordcloud' was built under R version 3.3.3

## Loading required package: RColorBrewer

data <- df_positive
dfCorpus = Corpus(DataframeSource(data))
wordcloud(dfCorpus, max.words =9,scale=c(4,.5), 
          random.order = FALSE,rot.per=.5,vfont=c("sans serif","plain"),colors="green")

## Warning in wordcloud(dfCorpus, max.words = 9, scale = c(4, 0.5),
## random.order = FALSE, : ecstatically could not be fit on page. It will not
## be plotted.

## Warning in wordcloud(dfCorpus, max.words = 9, scale = c(4, 0.5),
## random.order = FALSE, : reassurance could not be fit on page. It will not
## be plotted.

## Warning in wordcloud(dfCorpus, max.words = 9, scale = c(4, 0.5),
## random.order = FALSE, : worth-while could not be fit on page. It will not
## be plotted.

data <- df_negative
dfCorpus = Corpus(DataframeSource(data))
wordcloud(dfCorpus, max.words =9,scale=c(4,.5), 
          random.order = FALSE,rot.per=.5,vfont=c("sans serif","plain"),colors="red")

## Warning in wordcloud(dfCorpus, max.words = 9, scale = c(4, 0.5),
## random.order = FALSE, : harangue could not be fit on page. It will not be
## plotted.

## Warning in wordcloud(dfCorpus, max.words = 9, scale = c(4, 0.5),
## random.order = FALSE, : inconvenience could not be fit on page. It will not
## be plotted.

## Warning in wordcloud(dfCorpus, max.words = 9, scale = c(4, 0.5),
## random.order = FALSE, : lacking could not be fit on page. It will not be
## plotted.

## Warning in wordcloud(dfCorpus, max.words = 9, scale = c(4, 0.5),
## random.order = FALSE, : lukewarm could not be fit on page. It will not be
## plotted.

## Warning in wordcloud(dfCorpus, max.words = 9, scale = c(4, 0.5),
## random.order = FALSE, : ominously could not be fit on page. It will not be
## plotted.

## Warning in wordcloud(dfCorpus, max.words = 9, scale = c(4, 0.5),
## random.order = FALSE, : shortcoming could not be fit on page. It will not
## be plotted.

Step 2: We will collect writings from social media regrading the subjet matter:

Here we go for twitter, # you need to use your own key, which can be obtain from tweeter, when you create an app on it

library(twitteR)

## Warning: package 'twitteR' was built under R version 3.3.3

library(httr)

## Warning: package 'httr' was built under R version 3.3.3

## 
## Attaching package: 'httr'

## The following object is masked from 'package:NLP':
## 
##     content

# api_key <- "xxxxxxxxxxxxxxxxxxxxxxxxx"
# api_secret <- "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
# access_token <- "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
# access_token_secret <- "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"

It’s easier to first store the positive and negative words in your local drive

setup_twitter_oauth(api_key,api_secret,access_token,access_token_secret)

## [1] "Using direct authentication"

# Grab latest tweets
tweets_warriors <- searchTwitter('@warriors', n=5000)
tweets_cavs <- searchTwitter('@cavs', n=5000)

# Loop over tweets and extract text
library(plyr)

## Warning: package 'plyr' was built under R version 3.3.3

## 
## Attaching package: 'plyr'

## The following object is masked from 'package:twitteR':
## 
##     id

feed_warriors = laply(tweets_warriors, function(t) t$getText())
feed_cavs = laply(tweets_cavs, function(t) t$getText())

# Read in dictionary of positive and negative works
yay = scan('C:/Users/Janpu/Documents/R_files/positive-words.txt',
           what='character', comment.char=';')
boo = scan('C:/Users/Janpu/Documents/R_files/negative-words.txt',
           what='character', comment.char=';')
# Add a few twitter-specific negative phrases
bad_text = c(boo, 'wtf', 'epicfail', 'douchebag')
good_text = c(yay, 'upgrade', ':)', '#iVoted', 'voted')

Step 3: Time to do the comparison and give a score

Jeffrey Breen did a great piece on Airline sentiment analysis, from which many people including me ended up using his score.sentiment() function … https://github.com/jeffreybreen/twitter-sentiment-analysis-tutorial-201107/blob/master/R/sentiment.R.

options(warn=-1)
score.sentiment = function(sentences, good_text, bad_text, .progress='none')
{
  require(plyr)
  require(stringr)
  # we got a vector of sentences. plyr will handle a list
  # or a vector as an "l" for us
  # we want a simple array of scores back, so we use
  # "l" + "a" + "ply" = "laply":
  scores = laply(sentences, function(sentence, good_text, bad_text) {
    
    # clean up sentences with R's regex-driven global substitute, gsub():
    sentence = gsub('[[:punct:]]', '', sentence)
    sentence = gsub('[[:cntrl:]]', '', sentence)
    sentence = gsub('\\d+', '', sentence)
    #to remove emojis
    sentence <- iconv(sentence, 'UTF-8', 'ASCII')
    sentence = tolower(sentence)        
    # split into words. str_split is in the stringr package
    word.list = str_split(sentence, '\\s+')
    # sometimes a list() is one level of hierarchy too much
    words = unlist(word.list)
    
    # compare our words to the dictionaries of positive & negative terms
    pos.matches = match(words, good_text)
    neg.matches = match(words, bad_text)
    
    # match() returns the position of the matched term or NA
    # we just want a TRUE/FALSE:
    pos.matches = !is.na(pos.matches)
    neg.matches = !is.na(neg.matches)
    
    # and conveniently enough, TRUE/FALSE will be treated as 1/0 by sum():
    score = sum(pos.matches) - sum(neg.matches)
    
    return(score)
  }, good_text, bad_text, .progress=.progress )
  
  scores.df = data.frame(score=scores, text=sentences)
  return(scores.df)
}
the_cavs <- score.sentiment(feed_cavs, good_text, bad_text, .progress='text')

## Loading required package: stringr

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the_cavs$name <- 'Cavs'
the_warriors <- score.sentiment(feed_warriors, good_text, bad_text, .progress='text')

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the_warriors$name <- 'Warriors'

Step 4: Visulization

# Merge into one dataframe for plotting
plotdat <- rbind(the_cavs, the_warriors)

# Cut the text, just gets in the way
plotdat <- plotdat[c("name", "score")]


y <- as.factor(the_cavs$score)
x <- as.factor(the_warriors$score)

counts <- table(plotdat$name, plotdat$score)
barplot(counts, main="Sentiment Analysis on 5000 Tweets: 2017 NBA Championship",
        xlab="Number of Scores", col=c("darkblue","red"),
        legend = rownames(counts), beside=TRUE)

Step 4-1: We can count how many positive fans for each team

options(warn=-1)
score.sentiment = function(sentences, good_text, bad_text, .progress='none')
{
  require(plyr)
  require(stringr)
  # we got a vector of sentences. plyr will handle a list
  # or a vector as an "l" for us
  # we want a simple array of scores back, so we use
  # "l" + "a" + "ply" = "laply":
  scores = laply(sentences, function(sentence, good_text, bad_text) {
    
    # clean up sentences with R's regex-driven global substitute, gsub():
    sentence = gsub('[[:punct:]]', '', sentence)
    sentence = gsub('[[:cntrl:]]', '', sentence)
    sentence = gsub('\\d+', '', sentence)
    #to remove emojis
    sentence <- iconv(sentence, 'UTF-8', 'ASCII')
    sentence = tolower(sentence)        
    # split into words. str_split is in the stringr package
    word.list = str_split(sentence, '\\s+')
    # sometimes a list() is one level of hierarchy too much
    words = unlist(word.list)
    
    # compare our words to the dictionaries of positive & negative terms
    pos.matches = match(words, good_text)
    neg.matches = match(words, bad_text)
    
    # match() returns the position of the matched term or NA
    # we just want a TRUE/FALSE:
    pos.matches = !is.na(pos.matches)
    neg.matches = !is.na(neg.matches)
    
    # and conveniently enough, TRUE/FALSE will be treated as 1/0 by sum():
    score = sum(pos.matches) 
    
    return(score)
  }, good_text, bad_text, .progress=.progress )
  
  scores.df = data.frame(score=scores, text=sentences)
  return(scores.df)
}

the_warriors <- score.sentiment(feed_warriors, good_text, bad_text, .progress='text')

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the_warriors$name <- 'warriors'


the_cavs <- score.sentiment(feed_cavs, good_text, bad_text, .progress='text')

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the_cavs$name <- 'Cavs'

wp <- sum(the_warriors$score)
cat("positive fan for warriors = ",wp)

## positive fan for warriors =  721

cp <- sum(the_cavs$score)
cat ("positive fan for cavs = ",cp)

## positive fan for cavs =  1896

Step 4-2: We can count how many negative fans for each team

options(warn=-1)
score.sentiment = function(sentences, good_text, bad_text, .progress='none')
{
  require(plyr)
  require(stringr)
  # we got a vector of sentences. plyr will handle a list
  # or a vector as an "l" for us
  # we want a simple array of scores back, so we use
  # "l" + "a" + "ply" = "laply":
  scores = laply(sentences, function(sentence, good_text, bad_text) {
    
    # clean up sentences with R's regex-driven global substitute, gsub():
    sentence = gsub('[[:punct:]]', '', sentence)
    sentence = gsub('[[:cntrl:]]', '', sentence)
    sentence = gsub('\\d+', '', sentence)
    #to remove emojis
    sentence <- iconv(sentence, 'UTF-8', 'ASCII')
    sentence = tolower(sentence)        
    # split into words. str_split is in the stringr package
    word.list = str_split(sentence, '\\s+')
    # sometimes a list() is one level of hierarchy too much
    words = unlist(word.list)
    
    # compare our words to the dictionaries of positive & negative terms
    pos.matches = match(words, good_text)
    neg.matches = match(words, bad_text)
    
    # match() returns the position of the matched term or NA
    # we just want a TRUE/FALSE:
    pos.matches = !is.na(pos.matches)
    neg.matches = !is.na(neg.matches)
    
    # and conveniently enough, TRUE/FALSE will be treated as 1/0 by sum():
    score = sum(neg.matches) 
    
    return(score)
  }, good_text, bad_text, .progress=.progress )
  
  scores.df = data.frame(score=scores, text=sentences)
  return(scores.df)
}

the_warriors <- score.sentiment(feed_warriors, good_text, bad_text, .progress='text')

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the_warriors$name <- 'warriors'


the_cavs <- score.sentiment(feed_cavs, good_text, bad_text, .progress='text')

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the_cavs$name <- 'Cavs'

wn <- sum(the_warriors$score)
cat("negative fan for warriors = ",wn)

## negative fan for warriors =  143

cn <- sum(the_cavs$score)
cat ("negative fan for cavs = ",cn)

## negative fan for cavs =  372

Step 4-3: Simple Classification of Fans right before the Game

smoke <- matrix(c(cp,cn,wp,wn),ncol=2,byrow=TRUE)
colnames(smoke) <- c("Encorage","Criticize")
rownames(smoke) <- c("Cavs","Warriors")
smoke <- as.table(smoke)
smoke

##          Encorage Criticize
## Cavs         1896       372
## Warriors      721       143

barplot(smoke, main="Sentiment Analysis on 5000 Tweets: 2017 NBA Championship",
        xlab="Setiment of Fans", col=c("darkblue","red"),
        legend = rownames(smoke), beside=TRUE)