Domain-Specific Lexicon-Based Sentiment Analysis using Contextual Shifter Patterns

Sentiment lexicon plays a vital role in lexicon-based sentiment analysis. The lexicon-based method is often preferred because it leads to more explainable answers in comparison with many machine learning-based methods. However, lexicons that include only unigrams do not capture contextual information. To this end, we automatically generate domain-specific lexicons and manually develop contextual shifters. We show that for sentiment classification tasks in the economics and finance domain, the symbolic approach achieves competitive performance as the deep neural network. In addition, the symbolic approach provides understandable explanations. We will release the lexicons, shifter patterns and models to motivate future research in this direction.