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

TL;DR

This paper introduces a Hierarchical Interaction Network with a Rethinking mechanism that enhances document-level sentiment analysis by explicitly modeling interactions between summaries and documents, leading to improved sentiment classification accuracy.

Contribution

The paper proposes a novel Hierarchical Interaction Network combined with a sentiment-aware Rethinking mechanism to better capture subject and sentiment interactions for DSA.

Findings

HIN-SR outperforms state-of-the-art methods on three datasets.

Explicit interaction modeling improves sentiment representation.

Sentiment-based refinement enhances classification accuracy.

Abstract

Document-level Sentiment Analysis (DSA) is more challenging due to vague semantic links and complicate sentiment information. Recent works have been devoted to leveraging text summarization and have achieved promising results. However, these summarization-based methods did not take full advantage of the summary including ignoring the inherent interactions between the summary and document. As a result, they limited the representation to express major points in the document, which is highly indicative of the key sentiment. In this paper, we study how to effectively generate a discriminative representation with explicit subject patterns and sentiment contexts for DSA. A Hierarchical Interaction Networks (HIN) is proposed to explore bidirectional interactions between the summary and document at multiple granularities and learn subject-oriented document representations for sentiment classification. Furthermore, we design a Sentiment-based Rethinking mechanism (SR) by refining the HIN with sentiment label information to learn a more sentiment-aware document representation. We extensively evaluate our proposed models on three public datasets. The experimental results consistently demonstrate the effectiveness of our proposed models and show that HIN-SR outperforms various state-of-the-art methods.