Now that we are equipped with the key terms and concepts from the world of Sentiment Analysis, let us put our theory to the test. We have seen some major application areas for Sentiment Analysis and the challenges faced, in general, to perform such analytics. In this section we will perform Sentiment Analysis categorized into:
Polarity analysis: This will involve the scoring and aggregation of sentiment polarity using a labeled list of positive and negative words.
Classification-based analysis: In this approach we will make use of R's rich libraries to perform classification based on labeled tweets available for public usage. We will also discuss their performance and accuracy.
R has a very robust library for the extraction and manipulation of information from Twitter called TwitteR. As we saw in the previous chapter, we first need to create an application using Twitter's application management console before we can use TwitteR or any other library for sentiment...
