Improving Semantic Knowledge Base for Transfer Learning in Sentiment Analysis

Sentiment analysis deals with the computational treatment of opinion, sentiment, and subjectivity in text, has attracted a great deal of attention. Sentiment analysis has been widely used across a wide range of domains in recent years, such as information retrieval, question answering systems and social network. This paper presents a new method for improving the semantic knowledge base for sentiment classification in social web applications. It comprises the three steps. First, to identify sentiment terms. Next, to provide the context information from training corpus and ground this information to lexical resources such as WordNet. This Work applies to a transfer learning method called cross-domain sentiment classification. In Sentiment Analysis, transfer learning can be applied to transfer sentiment classification from one domain to another or building a bridge between two domains. This is achieved by learning the semantic knowledge base across the different domains. A model called AS_LDA is used for the sentiment classification. The performance of the proposed system improves the accuracy of the Sentiment Classifier to a significant extent. Key terms: WordNet, Sentiment Analysis, Cross-Domain sentiment Classification, Transfer Learning.