Now, the past posts we have understood the importance of using Twitter API, Basics of Twitter API and how we can access the Twitter API using R. Now we will get into analytics of how to do sentiment analysis with R with the library TwitteR. Before we do that we will try to do little understanding of Sentiment Analysis(some times also called as opinion mining) in a Q & A Format.
What is Sentiment Analysis ?
In simple words, Sentiment analysis is the task of identifying whether the opinion expressed in a text is positive or negative in general about a particular topic or context.
Can we have some examples?
a. I’m in a happy mood today, I go to beach. – Positive
b. I very much like R and its capabilities – Positive
c. I don’t like SPSS, its very complex to use – Negative
d. I feel Rapid Miner is easy to use and has good interface – Positive
Where it is being used or what are its applications?
With the lot of micro-blogging platforms available and business are well placed there, it’s important to understand that sentiment analysis on those platforms help understand the problems and feel of the customers.
- Understanding customer feedback received
- To arrive at happiness index of the customers
- Determining product recommendations
- Predicting Stock market moods
- As a competitive marketing tool
Steps for Sentiment Analysis?
How do we do the Practical implementation?
http://www.slideshare.net/jeffreybreen/r-by-example-mining-twitter-for (The one I Like the most).
You can look at the above given references for the practical implementation of Sentiment Analysis. Some of them may be outdated, in the next post we will do a practical step by step implementation of Sentiment Analysis with Twitter data using R.