Understanding What is Behind Sentiment Analysis – Part 2

Hint! Check Part I first, where we introduced a simple algorithm to analyze the sentiment of a given document. In this article we will talk about different modifications that might help us improve the performance of our classifier. To create a good classifier with the model described in Part I, we need a big and properly labelled corpus in order to compute a comprehensive word-sentiment occurrence table. In the training corpus, there should be statistically enough examples of each word in different contexts so the occurrences computed in the table can leverage a good approximation of their real probabilities (frequencies). There are several techniques aimed to reduce the dimensionality of the problem to make it more manageable.