After lot of difficulties my 3rd post on this topic in this weekend. In my first post we saw what is sentiment analysis and what are the steps involved in it. In my previous post we saw how to retrieve the tweets and store it in the File step by step. Now we will move on to the step of Sentiment analysis.
Goal: To do sentiment analysis on Airtel Customer support via Twitter in India.
In this Post: We will retrieve the Tweets which are retrieved and stored in the previous post and start doing the analysis. In this post I’m going to use the simple algorithm as used by Jeffrey Breen to determine the scores/moods of the particular brand in twitter.
We will use the opinion lexicon provided by him which is primarily based on Hu and Liu papers. You can visit their site for lot of useful information on sentiment analysis. We can determine the positive and negative words in the tweets, based on which scoring will happen.
Step 1: We will import the CSV file into R using read.csv and you can use the summary to display the summary of the dataframe.
Step 2: We can load the Positive words and Negative words and store it locally and can import using Scan function as given below:
Now we will look at the code for evaluating the Sentiment. This has been taken from http://jeffreybreen.wordpress.com/2011/07/04/twitter-text-mining-r-slides/. Thanks for the source code by Jeffrey.
We will test this sentiment.score function with some sample data.
In this step we have created test and added 3 sentences to it. This contains different words which may be positive or negative. Pass this “Test” to the score.sentiment function with pos_words and neg_words which we have loaded in the previous tests. Now you get the result score from the score.sentiment function against each sentence.
we will also try understand little more about this function and what it does:
a. Two libraries are loaded they are plyr and stringr. Both written by Hadley Wickham one of the great contributor to R. You can also learn more about plyr using this page or tutorial. You can also get more insights on split-apply-combine details here best place to start according to Hadley Wickham. You can think of it on analogy with Map-Reduce algorithm by Google which is used more in terms of Parallelism. stringr makes the string handling easier.
b. Next laply being used. You can learn more on what apply functions do here. In our case we pass on the sentences vector to the laply method. In simple terms this method takes each tweet and pass on to the function along with Positive and negative words and combines the result.
c. Next gsub helps to handle the replacements with the help using gsub(pattern, replacement, x).
d. Then convert the sentence to lowercase
e. Convert the sentences to words using the split methods and retrieve the appropriate scores using score methods.
Step 5: Now we will give the tweetsofaritel from airteltweetdata$text to the sentiment function to retrieve the score.
Step 6: We will see the summary of the scores and its histogram:
The histogram outcome:
It shows the most of the response out of 1499 is negative about airtel.
Disclaimer: Please note that this is only sample data which is analyzed only for the purpose of educational and learning purpose. It’s not to target any brand or influence any brand.