This project uses Lexicon-based approach for sentimental analysis of 1000 recent tweets of 4 countries - USA, India, China and Syria. The general idea is to calculate a sentiment score for each tweet and thereby find out how positive or negative the tweet is.
The sentiment score for each tweet is calculated as follows:
Score = Number of positive words - Number of negative words
- If Score > 0, the tweet has an overall 'positive opinion'
- If Score < 0, the tweet has an overall 'negative opinion'
- If Score = 0, the tweet is said to have a 'neutral opinion'
In order to count the number of positive and negative words in a tweet, an Opinion Lexicon in English is used. It is provided by Hu & Liu and has Positive and Negative Words list. It is taken from: http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
Example:
mytest= c("I love John Cena", "Cute cat!", "That was a bad night", "She loves bad boys")
testsentiment = score.sentiment(mytest, pos, neg)
testsentiment
Output:
| text | score | |
|---|---|---|
| 1 | I love John Cena | 1 |
| 2 | Cute cat! | 1 |
| 3 | That was a bad night | -1 |
| 4 | She loves bad boys | 0 |
Note :
- The code for Twitter-R connection and analysis can be found here
- The results are shown in the form of Box Plots, histograms and Word Clouds
- Twitter allows only 15 scrapes in 15 minutes
- The Twitter Search API searches against a sampling of recent Tweets published in the past 7 days. So only tweets in the last 7 days can be mined
- Merry Christmas :P