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Document-level Sentiment Analysis (DSA) is more challenging due to vaguesemantic links and complicate sentiment information. Recent works have beendevoted to leveraging text summarization and have achieved promising results.However, these summarization-based methods did not take full advantage of thesummary including ignoring the inherent interactions between the summary anddocument. As a result, they limited the representation to express major pointsin the document, which is highly indicative of the key sentiment. In thispaper, we study how to effectively generate a discriminative representationwith explicit subject patterns and sentiment contexts for DSA. A HierarchicalInteraction Networks (HIN) is proposed to explore bidirectional interactionsbetween the summary and document at multiple granularities and learnsubject-oriented document representations for sentiment classification.Furthermore, we design a Sentiment-based Rethinking mechanism (SR) by refiningthe HIN with sentiment label information to learn a more sentiment-awaredocument representation. We extensively evaluate our proposed models on threepublic datasets. The experimental results consistently demonstrate theeffectiveness of our proposed models and show that HIN-SR outperforms variousstate-of-the-art methods.

Hierarchical Interaction Networks with Rethinking Mechanism for Document-level Sentiment Analysis