Domain-Based Lexicon Enhancement for Sentiment Analysis

General knowledge sentiment lexicons have the advantage of wider term coverage. However, such lexicons typically have inferior performance for sentiment classification compared to using domain focused lexicons or machine learning classifiers. Such poor performance can be attributed to the fact that some domain-specific sentiment-bearing terms may not be available from a general knowledge lexicon. Similarly, there is difference in usage of the same term between domain and general knowledge lexicons in some cases. In this paper, we propose a technique that uses distant-supervision to learn a domain focused sentiment lexicon. The technique further combines general knowledge lexicon with the domain focused lexicon for sentiment analysis. Implementation and evaluation of the technique on Twitter text show that sentiment analysis benefits from the combination of the two knowledge sources. The technique also performs better than state-of-the-art machine learning classifiers trained with distantsupervision dataset.