A document-level sentiment analysis approach using artificial neural network and sentiment lexicons

Abstract

The abundance of discussion forums, Weblogs, e-commerce portals, social networking, product review sites and content sharing sites has facilitated flow of ideas and expression of opinions. The user-generated text content on Internet and Web 2.0 social media can be a rich source of sentiments, opinions, evaluations, and reviews. Sentiment analysis or opinion mining has become an open research domain that involves classifying text documents based on the opinion expressed, about a given topic, being positive or negative. This paper proposes a sentiment classification model using back-propagation artificial neural network (BPANN). Information Gain, and three popular sentiment lexicons are used to extract sentiment representing features that are then used to train and test the BPANN. This novel approach combines the strength of BPANN in classification accuracy with intrinsic subjectivity knowledge available in the sentiment lexicons. The results obtained from experiments on the movie and hotel review corpora have shown that the proposed approach has been able to reduce dimensionality, while producing accurate results for sentiment based classification of text.