Combining User-based and Global Lexicon Features for Sentiment Analysis in Twitter

Generally speaking, sentiment lexicons employed in the majority of current sentiment analysis systems are trained globally from public data stream source or other large independent corpus. However, sentiments are rather subjective and personal states of mind that the individuality and diversity of characteristics, particular writing habit and idiolect could play a crucial role in the judgment of sentiment expressed by a specific user. In this paper, we present a novel feature construction method to combine user-based and global lexicon features in sentiment analysis for short social media text. After the creation of user-based sentiment lexicons from user-timeline corpus, a rule-based fusing approach is adopted subsequently to generate user-based lexicon features in combination with general lexicon features. Experiments show that user-based features may capture potential user preferences hence adjusting the bias caused by representing an individual's sentiment with an averaged lexicon score, and our proposed method yield better results in comparison with some of the state-of-the-art sentiment analysis systems in twitter.