sentiment analysis

Doing a Sentiment Analysis on Tweets (Part 2)

INTRO This is post is a continuation of my last post. There I pulled tweets from Twitter related to “Comcast email,” got rid of the junk, and removed the unnecessary/unwanted data. Now that I have the tweets, I will further clean the text and subject it to two different analyses: emotion and polarity. WHY DOES THIS MATTER Before I get started, I thought it might be a good idea to talk about WHY I am doing this (besides the fact that I learned a new skill and want to show it off and get feedback). This yet incomplete project was devised for two reasons: Understand the overall customer sentiment about the product I support Create an early warning system to help identify when things are going wrong on the platform Keeping the customer voice at the forefront of everything we do is tantamount to providing the best experience for the users of our platform. Identifying trends in sentiment and emotion can help inform the team in many ways, including seeing the reaction to new features/releases (i.e. – seeing a rise in comments about a specific addition from a release) and identifying needed changes to current functionality (i.e. – users who continually comment about a specific behavior of the application) and improvements to user experience (i.e. – trends in comments about being unable to find a certain feature on the site). Secondarily, this analysis can act as an early warning system when there are issues with the platform (i.e. – a sudden spike in comments about the usability of a mobile device). Now that I’ve explained why I am doing this (which I probably should have done in this sort of detail the first post), let’s get into how it is actually done… STEP ONE: STRIPPING THE TEXT FOR ANALYSIS There are a number of things included in tweets that dont matter for the analysis. Things like twitter handles, URLs, punctuation… they are not necessary to do the analysis (in fact, they may well confound it). This bit of code handles that cleanup. For those following the scripts on GitHub, this is part of my tweet_clean.R script. Also, to give credit where it is due: I’ve borrowed and tweaked the code from Andy Bromberg’s blog to do this task. library(stringr) ##Does some of the text editing ##Cleaning up the data some more (just the text now) First grabbing only the text text <- paredTweetList$Tweet # remove retweet entities text <- gsub("(RT|via)((?:\\b\\W*@\\w+)+)", "", text) # remove at people text <- gsub("@\\w+", "", text) # remove punctuation text <- gsub("[[:punct:]]", "", text) # remove numbers text <- gsub("[[:digit:]]", "", text) # remove html links text <- gsub("http\\w+", "", text) # define "tolower error handling" function try.error <- function(x) { # create missing value y <- NA # tryCatch error try_error <- tryCatch(tolower(x), error=function(e) e) # if not an error if (!inherits(try_error, "error")) y <- tolower(x) # result return(y) } # lower case using try.error with sapply text <- sapply(text, try.error) # remove NAs in text text <- text[!is.na(text)] # remove column names names(text) <- NULL STEP TWO: CLASSIFYING THE EMOTION FOR EACH TWEET So now the text is just that: only text. The punctuation, links, handles, etc. have been removed. Now it is time to estimate the emotion of each tweet. Through some research, I found that there are many posts/sites on Sentiment Analysis/Emotion Classification that use the “Sentiment” package in R. I thought: “Oh great – a package tailor made to solve the problem for which I want an answer.” The problem is that this package has been deprecated and removed from the CRAN library. To get around this, I downloaded the archived package and pulled the code for doing the emotion classification. With some minor tweaks, I was able to get it going. This can be seen in its entirety in the classify_emotion.R script. You can also see the “made for the internet” version here: library(RTextTools) library(tm) algorithm <- "bayes" prior <- 1.0 verbose <- FALSE matrix <- create_matrix(text) lexicon <- read.csv("./data/emotions.csv.gz",header=FALSE) counts <- list(anger=length(which(lexicon[,2]=="anger")), disgust=length(which(lexicon[,2]=="disgust")), fear=length(which(lexicon[,2]=="fear")), joy=length(which(lexicon[,2]=="joy")), sadness=length(which(lexicon[,2]=="sadness")), surprise=length(which(lexicon[,2]=="surprise")), total=nrow(lexicon)) documents <- c() for (i in 1:nrow(matrix)) { if (verbose) print(paste("DOCUMENT",i)) scores <- list(anger=0,disgust=0,fear=0,joy=0,sadness=0,surprise=0) doc <- matrix[i,] words <- findFreqTerms(doc,lowfreq=1) for (word in words) { for (key in names(scores)) { emotions <- lexicon[which(lexicon[,2]==key),] index 0) { entry <- emotions[index,] category <- as.character(entry[[2]]]) count <- counts[[category]] score <- 1.0 if (algorithm=="bayes") score <- abs(log(score*prior/count)) if (verbose) { print(paste("WORD:",word,"CAT:", category,"SCORE:",score)) } scores[[category]] <- scores[[category]]+score } } } if (algorithm=="bayes") { for (key in names(scores)) { count <- counts[[key]] total <- counts[["total"]] score <- abs(log(count/total)) scores[[key]] <- scores[[key]]+score } } else { for (key in names(scores)) { scores[[key]] <- scores[[key]]+0.000001 } } best_fit <- names(scores)[which.max(unlist(scores))] if (best_fit == "disgust" && as.numeric(unlist(scores[2]))-3.09234 < .01) best_fit <- NA documents <- rbind(documents, c(scores$anger, scores$disgust, scores$fear, scores$joy, scores$sadness, scores$surprise, best_fit)) } colnames(documents) <- c("ANGER", "DISGUST", "FEAR", "JOY", "SADNESS", "SURPRISE", "BEST_FIT") Here is a sample output from this code: ANGER DISGUST FEAR JOY SADNESS SURPRISE BEST_FIT “1.46871776464786” “3.09234031207392” “2.06783599555953” “1.02547755260094” “7.34083555412328” “7.34083555412327” “sadness” “7.34083555412328” “3.09234031207392” “2.06783599555953” “1.02547755260094” “1.7277074477352” “2.78695866252273” “anger” “1.46871776464786” “3.09234031207392” “2.06783599555953” “1.02547755260094” “7.34083555412328” “7.34083555412328” “sadness” Here you can see that the initial author is using naive Bayes (which honestly I don’t yet understand) to analyze the text. I wanted to show a quick snipet of how the analysis is being done “under the hood.” For my purposes though, I only care about the emotion outputted and the tweet it is analyzed from. emotion <- documents[, "BEST_FIT"]` This variable, emotion, is returned by the classify_emotion.R script. CHALLENGES OBSERVED In addition to not fully understanding the code, the emotion classification seems to only work OK (which is pretty much expected… this is a canned analysis that hasn’t been tailored to my analysis at all). I’d like to come back to this one day to see if I can do a better job analyzing the emotions of the tweets. STEP THREE: CLASSIFYING THE POLARITY OF EACH TWEET Similarly to what we saw in step 5, I will use the cleaned text to analyze the polarity of each tweet. This code is also from the old R Packaged titled “Sentiment.” As with above, I was able to get the code working with only some minor tweaks. This can be seen in its entirety in the classify_polarity.R script. Here it is, too: algorithm <- "bayes" pstrong <- 0.5 pweak <- 1.0 prior <- 1.0 verbose <- FALSE matrix <- create_matrix(text) lexicon <- read.csv("./data/subjectivity.csv.gz",header=FALSE) counts <- list(positive=length(which(lexicon[,3]=="positive")), negative=length(which(lexicon[,3]=="negative")), total=nrow(lexicon)) documents <- c() for (i in 1:nrow(matrix)) { if (verbose) print(paste("DOCUMENT",i)) scores <- list(positive=0,negative=0) doc <- matrix[i,] words <- findFreqTerms(doc, lowfreq=1) for (word in words) { index 0) { entry <- lexicon[index,] polarity <- as.character(entry[[2]]) category <- as.character(entry[[3]]) count <- counts[[category]] score <- pweak if (polarity == "strongsubj") score <- pstrong if (algorithm=="bayes") score <- abs(log(score*prior/count)) if (verbose) { print(paste("WORD:", word, "CAT:", category, "POL:", polarity, "SCORE:", score)) } scores[[category]] <- scores[[category]]+score } } if (algorithm=="bayes") { for (key in names(scores)) { count <- counts[[key]] total <- counts[["total"]] score <- abs(log(count/total)) scores[[key]] <- scores[[key]]+score } } else { for (key in names(scores)) { scores[[key]] <- scores[[key]]+0.000001 } } best_fit <- names(scores)[which.max(unlist(scores))] ratio <- as.integer(abs(scores$positive/scores$negative)) if (ratio==1) best_fit <- "neutral" documents <- rbind(documents,c(scores$positive, scores$negative, abs(scores$positive/scores$negative), best_fit)) if (verbose) { print(paste("POS:", scores$positive,"NEG:", scores$negative, "RATIO:", abs(scores$positive/scores$negative))) cat("\n") } } colnames(documents) <- c("POS","NEG","POS/NEG","BEST_FIT") Here is a sample output from this code: POS NEG POS/NEG BEST_FIT “1.03127774142571” “0.445453222112551” “2.31512017476245” “positive” “1.03127774142571” “26.1492093145274” “0.0394381997949273” “negative” “17.9196623384892” “17.8123396772424” “1.00602518608961” “neutral” Again, I just wanted to show a quick snipet of how the analysis is being done “under the hood.” I only care about the polarity outputted and the tweet it is analyzed from. polarity <- documents[, "BEST_FIT"] This variable, polarity, is returned by the classify_polarity.R script. CHALLENGES OBSERVED As with above, this is a stock analysis and hasn’t been tweaked for my needs. The analysis does OK, but I want to come back to this again one day to see if I can do better. QUICK CONCLUSION So… Now I have the emotion and polarity for each tweet. This can be useful to see on its own, but I think is more worthwhile in aggregate. In my next post, I’ll show that. Also in the next post- I’ll also show an analysis of the word count with a wordcloud… This gets into the secondary point of this analysis. Hypothetically, I’d like to see common issues bubbled up through the wordcloud.

Doing a Sentiment Analysis on Tweets (Part 1)

INTRO So… This post is my first foray into the R twitteR package. This post assumes that you have that package installed already in R. I show here how to get tweets from Twitter in preparation for doing some sentiment analysis. My next post will be the actual sentiment analysis. For this example, I am grabbing tweets related to “Comcast email.” My goal of this exercise is to see how people are feeling about the product I support. STEP 1: GETTING AUTHENTICATED TO TWITTER First, you’ll need to create an application at Twitter. I used this blog post to get rolling with that. This post does a good job walking you through the steps to do that. Once you have your app created, this is the code I used to create and save my authentication credentials. Once you’ve done this once, you need only load your credentials in the future to authenticate with Twitter. library(twitteR) ## R package that does some of the Twitter API heavy lifting consumerKey <- "INSERT YOUR KEY HERE" consumerSecret <- "INSERT YOUR SECRET HERE" reqURL <- "https://api.twitter.com/oauth/request_token " accessURL <- "https://api.twitter.com/oauth/access_token " authURL <- "https://api.twitter.com/oauth/authorize " twitCred <- OAuthFactory$new(consumerKey = consumerKey, consumerSecret = consumerSecret, requestURL = reqURL, accessURL = accessURL, authURL = authURL) twitCred$handshake() save(cred, file="credentials.RData") STEP 2: GETTING THE TWEETS Once you have your authentication credentials set, you can use them to grab tweets from Twitter. The next snippets of code come from my scraping_twitter.R script, which you are welcome to see in it’s entirety on GitHub. ##Authentication load("credentials.RData") ##has my secret keys and shiz registerTwitterOAuth(twitCred) ##logs me in ##Get the tweets about "comcast email" to work with tweetList <- searchTwitter("comcast email", n = 1000) tweetList <- twListToDF(tweetList) ##converts that data we got into a data frame As you can see, I used the twitteR R Package to authenticate and search Twitter. After getting the tweets, I converted the results to a Data Frame to make it easier to analyze the results. STEP 3: GETTING RID OF THE JUNK Many of the tweets returned by my initial search are totally unrelated to Comcast Email. An example of this would be: “I am selling something random… please email me at myemailaddress@comcast.net” The tweet above includes the words email and comcast, but has nothing to actually do with Comcast Email and the way the user feels about it, other than they use it for their business. So… based on some initial, manual, analysis of the tweets, I’ve decided to pull those tweets with the phrases: “fix” AND “email” in them (in that order) “Comcast” AND “email” in them in that order “no email” in them Any tweet that comes from a source with “comcast” in the handle “Customer Service” AND “email” OR the reverse (“email” AND “Customer Service”) in them This is done with this code: ##finds the rows that have the phrase "fix ... email" in them fixemail <- grep("(fix.*email)", tweetList$text) ##finds the rows that have the phrase "comcast ... email" in them comcastemail <- grep("[Cc]omcast.*email", tweetList$text) ##finds the rows that have the phrase "no email" in them noemail <- grep("no email", tweetList$text) ##finds the rows that originated from a Comcast twitter handle comcasttweet <- grep("[Cc]omcast", tweetList$screenName) ##finds the rows related to email and customer service custserv <- grep("[Cc]ustomer [Ss]ervice.*email|email.*[Cc]ustomer [Ss]ervice", tweetList$text) After pulling out the duplicates (some tweets may fall into multiple scenarios from above) and ensuring they are in order (as returned initially), I assign the relevant tweets to a new variable with only some of the returned columns. The returned columns are: text favorited favoriteCount replyToSN created truncated replyToSID id replyToUID statusSource screenName retweetCount isRetweet retweeted longitude latitude All I care about are: text created statusSource screenName This is handled through this tidbit of code: ##combine all of the "good" tweets row numbers that we greped out above and ##then sorts them and makes sure they are unique combined <- c(fixemail, comcastemail, noemail, comcasttweet, custserv) uvals <- unique(combined) sorted <- sort(uvals) ##pull the row numbers that we want, and with the columns that are important to ##us (tweet text, time of tweet, source, and username) paredTweetList <- tweetList[sorted, c(1, 5, 10, 11)] STEP 4: CLEAN UP THE DATA AND RETURN THE RESULTS Lastly, for this first script, I make the sources look nice, add titles, and return the final list (only a sample set of tweets shown): ##make the device source look nicer paredTweetList$statusSource <- sub("<.*\">", "", paredTweetList$statusSource) paredTweetList$statusSource <- sub("</a>", "", paredTweetList$statusSource) ##name the columns names(paredTweetList) <- c("Tweet", "Created", "Source", "ScreenName") paredTweetList Tweet created statusSource screenName Dear Mark I am having problems login into my acct REDACTED@comcast.net I get no email w codes to reset my password for eddygil HELP HELP 2014-12-23 15:44:27 Twitter Web Client riocauto @msnbc @nbc @comcast pay @thereval who incites the murder of police officers. Time to send them a message of BOYCOTT! Tweet/email them NOW 2014-12-23 14:52:50 Twitter Web Client Monty_H_Mathis Comcast, I have no email. This is bad for my small business. Their response “Oh, I’m sorry for that”. Problem not resolved. #comcast 2014-12-23 09:20:14 Twitter Web Client mathercesul CHALLENGES OBSERVED As you can see from the output, sometimes some “junk” still gets in. Something I’d like to continue working on is a more reliable algorithm for identifying appropriate tweets. I also am worried that my choice of subjects is biasing the sentiment.