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Sentiment classification is classification of reviews into positive or negative depends on the sentiment words expressed in reviews. Automatic sentiment classification is necessary in various applications such as market analysis, opinion mining, contextual advertisement and opinion summarization. However, sentiments are expressed differently in different domain and annotating label for every domain of interest is expensive and time consuming. In cross domain sentiment classification, a sentiment classifier trained in source domain is applied to classify reviews of target domain, always produce low performance due to the occurrence of features mismatch between source domain and target domain. The proposed method develops solution to feature mismatch problem in cross domain sentiment classification by creating enhanced sentiment sensitive thesaurus using wiktionary. The enhanced sentiment sensitive thesaurus aligns different words in expressing the same sentiment not only from different domains of reviews and from wiktionary to increase the classification performance in target domain. In this paper, the proposed method describes the method of construction of enhanced sentiment sensitive thesaurus which will be useful for cross domain sentiment classification.

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