Context-sensitive lexicon for imbalanced text sentiment classification using bidirectional LSTM

Sentiment lexicon is a reliable resource in computing sentiment classification. However, a general purpose lexicon alone is not sufficient, since text sentiment classification is perceived as a context-dependent task in the literature. On the contrary, we observe that many people tend to imitate others while writing reviews. As such, the subject of all the public opinion towards an entity ends up as an imbalanced corpus. In this paper, we intend to induce a context-based lexicon as a resource to explore imbalanced text sentiment classification. This method addresses the above mentioned two critical problems in text sentiment classification. First, it identifies subjective words relative to the context and computes the weight scores for subjective terms and full review. Also, in recent years, the application of RNNs to a variety of problems has been incredible, especially in natural language processing tasks. Thus, we take the advantages of the context-based lexicon as well as a bidirectional LSTM to handle text sentiment classification. Second, it deals imbalanced data by deploying a text based oversampling method for creating new synthetic text samples. The reason behind using a text based oversampling method is to make use of semantics of the information while creating new text samples. Experimental results prove that leveraging sentiment lexicon relative to the context and application of Bidiricetional LSTM with text based oversampling is useful in imbalanced text sentiment classification and in achieving state-of-the-art results over deep neural learning model baselines.