Sentiment Analysis on Twitter with Viralheat API

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Hi there!

Some time ago I published a post about doing a sentiment analysis on Twitter. I used two wordlists to do so; one with positive and one with negative words. For the first try of a sentiment analysis it is surely a good way to start but if you want to receive more accurate sentiments you should use an external API. And that´s what we do in this tutorial. But before we start you should take a look at the authentication tutorial and go through the steps.

The Viralheat API

The Viralheat sentiment API receives more than 300M calls per week. And this huge amount of calls makes this API become better and better. Everytime a company for example using this API notices that a tweet was analyzed wrong, lets say it was a positive tweet but the API said it is neutral, the user can correct it and the API can use this knowledge for the next time.

Viralheat registration

You can reach the Viralheat API with a free account. This account includes 1000calls/day what should be enough for starting. Just go to the Viralheat developer Center and register yourself:

Viralheat Developer Center

Then you can generate your free API key we´ll need later.


The getSentiment() function

First import the needed packages for our analysis:


The getSentiment() function handles the queries we send to the API and splits the positive and negative statements out of the JSON reply and returns them in a list.

getSentiment <- function (text, key){

text <- URLencode(text);

#save all the spaces, then get rid of the weird characters that break the API, then convert back the URL-encoded spaces.
text <- str_replace_all(text, "%20", " ");
text <- str_replace_all(text, "%\\d\\d", "");
text <- str_replace_all(text, " ", "%20");

if (str_length(text) > 360){
text <- substr(text, 0, 359);

data <- getURL(paste("", key, "&text=",text, sep=""))

js <- fromJSON(data, asText=TRUE);

# get mood probability
score = js$prob

# positive, negative or neutral?
if (js$mood != "positive")
if (js$mood == "negative") {
score = -1 * score
} else {
# neutral
score = 0

return(list(mood=js$mood, score=score))

The clean.text() function

We need this function because of the problems occurring when the tweets contain some certain characters and to remove characters like “@” and “RT”.

clean.text <- function(some_txt)
some_txt = gsub("(RT|via)((?:\\b\\W*@\\w+)+)", "", some_txt)
some_txt = gsub("@\\w+", "", some_txt)
some_txt = gsub("[[:punct:]]", "", some_txt)
some_txt = gsub("[[:digit:]]", "", some_txt)
some_txt = gsub("http\\w+", "", some_txt)
some_txt = gsub("[ \t]{2,}", "", some_txt)
some_txt = gsub("^\\s+|\\s+$", "", some_txt)

# define "tolower error handling" function
try.tolower = function(x)
y = NA
try_error = tryCatch(tolower(x), error=function(e) e)
if (!inherits(try_error, "error"))
y = tolower(x)

some_txt = sapply(some_txt, try.tolower)
some_txt = some_txt[some_txt != ""]
names(some_txt) = NULL

Let´s start

Ok now we have our functions, all packages and the API key.

In the first step we need the tweets. We do this with searchTwitter() function as usual.

# harvest tweets
tweets = searchTwitter("iphone5", n=200, lang="en")

In my example I used the keyword “iphone5″. Of course you can use whatever you want.

In the next steps we have to extract the text from the text and remove the characters with the clean_tweet() function. We just call these functions with:

tweet_txt = sapply(mc_tweets, function(x) x$getText())
tweet_clean = clean.text(tweet_txt)
mcnum = length(tweet_clean)
tweet_df = data.frame(text=tweet_clean, sentiment=rep("", mcnum), score=1:mcnum, stringsAsFactors=FALSE)

Do the analysis

We come to our final step: the analysis. We call the getSentiment() with the text of every tweet and wait for the answer to save it to a list. So this can cost some time. Just replace API-KEY with your Viralheat API key.

sentiment = rep(0, mcnum)
for (i in 1:mcnum)
tmp = getSentiment(tweet_clean[i], "API-KEY")
tweet_df$sentiment[i] = tmp$mood
tweet_df$score[i] = tmp$score

That´s it! Now we have our analyzed tweets in the tweet_df list and you can show your results with


Sentiment Results


Sometimes the API breaks when receiving certain character. I couldn´t figure out why , but as soon as I know it  I will update this tutorial.

Please also note that sentiment analysis can just give you a roughly overview of the mood.

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