Interpretable Word-Level Context-Based Sentiment Analysis

We propose a fine-grained attention-based multiple instance classification (FAMIC) model for interpretable word-level sentiment analysis (SA) using only document-level sentiment labels. By operating at the word level, FAMIC enhances interpretability while maintaining competitive performance in document-level classification. The model generates interpretable outputs such as contextual weighting, word neutrality, and negation cues, offering insights into how context shapes sentiment and how the model arrives at its predictions. FAMIC is built on a straightforward yet effective architecture that combines a multiple instance classification framework with self-attention and positionally encoded self-attention blocks. This design enables the model to capture both local and global contextual dependencies, supporting nuanced sentiment interpretation. We evaluate FAMIC on two sentiment classification datasets and provide an extensive analysis of its interpretability and performance.