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Sentiment Classification in Multi-Domain adaptation Using Sentiment Sensitive Thesaurus

Nancy S, Uma maheswaran S,Askerunisa A

Sentiment Analysis is an emerging field in Natural Language Processing (NLP) with very interesting application such as opinion mining, Opinion Summarization, Market Analysis. Sentiment Classifier trained for a single domain when used to classify reviews on a different domain results in poor performance. To overcome the problem in single domain classification, Sentiment Sensitive distributional thesaurus is created using unlabeled data for both source and target domains. A Binary Classifier was constructed using reviews to classify the SST as a positive and negative review and by using the created thesaurus the feature vectors are expanded during train and test times using Cosine Similarity and Point wise Mutual Information (PMI).The Performance of Multi-Domain and Single Domain Sentiment classification were compared and the results show that the Multi-Domain Adaptation outperforms numerous baselines.