Hierarchical Interaction Networks with Rethinking Mechanism for Document-level Sentiment Analysis
Table 2: Experimental results on three datasets. Evaluation metric is accuracy. - means not available. The best results are in bold. ∗ represents that the result is reported in the corresponding reference.
['[BOLD] Models', '[BOLD] Toys & Games', '[BOLD] Sports & Outdoors', '[BOLD] News']
Our proposed models perform the best among all baselines on three datasets, which reveals the effectiveness and advancement for DSA. Then, we compare our models with summary-based methods including HSSC and SAHSSC. It is not surprising since HIN takes advantage of the user-generated summary via modeling the interactions between the summary and document at multiple granularities levels and explores more discriminative features.