Traditional approaches to sentiment classification rely on lexical features, syntax-based features or a combination of the two. Word senses used as features show promise, we also examine the possibility of using similarity metrics defined on WordNet to address the problem of not finding a sense in the training corpus. Different methods of sentiment classification are also described in it. Thus is provides a broad way of analyzing the methods and making conclusion provided by these methods. Sentiment Analysis is a Natural Language Processing and Information Extraction task that aims to obtain writer’s feelings expressed in positive or negative comments, by analyzing a large numbers of documents. Generally speaking, sentiment analysis aims to determine the attitude of a speaker or writer with respect to some topic of a document. In recent years, the exponential increase in the Internet usage and exchange of public opinion is the driving force behind Sentiment Analysis today.
Corpus, Gloss, Synset ,WordNet
[1] “A systematic Approach towards the Solution of the Polysemy Problem in Natural Language Processing” ,Abed Alhakim Freihat April 2011.
[2] “Approach to Sentiment Analysis: Analytical Categories and Issues of Automation”, Repindex.
[3] “HarnessingWordNet Senses for Supervised Sentiment Classification”,Balamurali A,Aditya Joshi,Pushpak Bhattacharyya IITB-Monash Research Academy, IIT Bombay Dept. of Computer Science and Engineering, IIT Bombay.
[4] “Movie Review Mining: a Comparison between Supervised and Unsupervised Classification Approaches”,Pimwadee Chaovalit Department of Information Systems University of Maryland, Baltimore County Lina Zhou Department of Information Systems University of Maryland, Baltimore County.
[5] Peter D. Turney, "Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews," presented at the Association for Computational Linguistics 40th Anniversary Meeting, New Brunswick, N.J., 2002.
[6] “Sentiment Analysis, Indian Institute of Technology”,Subhabrata Mukherjee, Bombay Department of Computer Science and Engineering, June 29, 2012.
[7] “Sentiment Classification in Movie Reviews”,An Approach Using Subjectivity Filtering Daniel Pomerantz, McGill University.
[8] “Sentiment Classification of Reviews Using SentiWordNet”, Ohana, B., Tierney, B.: Sentiment classification of reviews using SentiWordNet. 9th. IT&T Conference, Dublin Institute of Technology, Dublin, Ireland.
