Interpretable Word-Level Context-Based Sentiment Analysis

We propose an attention-based multiple instance classification model (AMIC) to conduct interpretable word-level sentiment analysis (SA) using only document sentiment labels. The word-level SA adds more interpretability compared to other models while maintaining competitive performance at the document level. Furthermore, we decompose our model into interpretable outputs that provide context weighting, indication of word neutrality, and negation. This structure provides insights on how context influences sentiment and the inner workings in the model’s decision-making process. AMIC is built on a straightforward modeling framework (i.e., multiple instance classification model) which incorporates blocks of self-attention and positional encoded self-attention to achieve competitive prediction performance. The architecture is transparent yet effective at conducting interpretable SA. Model performance is reported on two document sentiment classification datasets, with extensive analysis of model interpretation.