Published Paper Details:

In this evolving technological environment there are many useful web applications developing at a faster rate. Sentiment analysis is an area of text classification that began around 2001 and has recently been receiving a lot of attention from researchers. Sentiment analysis involves analyzing textual datasets which contain opinions (e.g., social media, blogs, discussion groups, and internet forums) with the objective of classifying the opinions as positive, negative, or neutral. Various measures, including Information gain, simple chi-square, feature relation network, log-likelihood ratio, and minimum frequency thresholds, have previously been used as feature selection methods in sentiment analysis and classification. In this report, we investigate the performance of categorical proportional difference, a novel feature selection method used for text classification. While categorical proportional difference has previously been shown by others to be useful in sentiment classification of text documents, here we apply sentiment on online reviews. Online reviews differ from typical text documents in that they contain few features (i.e., they are short documents). We apply weighting schemes, SentiWordNet scores, information gain to classify the reviews as either positive or negative.