Classifying public opinion using SentiWordnet - Cheng, Charibeth K. (Philippine computing journal)
A large amount of opinions, such as those found in blogs, fora and product reviews, are being uploaded daily as the Web continues to develop. A rich source of information may be found in these content, but they are under utilized due to difficulty in discovering and monitoring them. Existing natural language processing techniques may be tapped to organize these content making them useful for decision making. This paper focuses on the development of a system that uses text-processing techniques in organizing the sentiment of public commentaries. Current systems are able to differentiate facts from opinions, as well as classify these opinions based on their sentiment. Clustering algorithms have been used to group related objects together. The system Vox Pop combines these three functions through lexicon-based approaches on opinion detection, sentiment classification and clustering. Opinions are differentiated from facts using a list of opinions markers. These Opinions are then classified by sentiment (i.e., positive, negative or neutral) using scores produced by SentiWordNet. Comments classified under the same sentiments are further organized into groups using k-Means clustering. The detection, classification and clustering modules have accuracy rates of 50%, 50.5% and 53.85%, respectively, when compared to expert evaluations.