From Stars to Insights: Exploration and Implementation of Unified Sentiment Analysis with Distant Supervision

• URL: http://arxiv.org/abs/2305.01710v3
• Date: Tue, 26 Nov 2024 18:40:56 GMT
• Title: From Stars to Insights: Exploration and Implementation of Unified Sentiment Analysis with Distant Supervision
• Authors: Wenchang Li, John P. Lalor, Yixing Chen, Vamsi K. Kanuri,
• Abstract summary: This paper introduces Unified Sentiment Analysis (Uni-SA), a novel learning paradigm that unifies ACD, ACSA, and RP into a coherent framework. We propose the Distantly Supervised Pyramid Network (DSPN), which employs a pyramid structure to capture sentiment at word, aspect, and document levels. Our findings validate DSPN's effectiveness and efficiency, establishing a robust, resource-efficient, unified framework for sentiment analysis.
• Score: 1.940999549833078
• License:
• Abstract: Sentiment analysis is integral to understanding the voice of the customer and informing businesses' strategic decisions. Conventional sentiment analysis involves three separate tasks: aspect-category detection (ACD), aspect-category sentiment analysis (ACSA), and rating prediction (RP). However, independently tackling these tasks can overlook their interdependencies and often requires expensive, fine-grained annotations. This paper introduces Unified Sentiment Analysis (Uni-SA), a novel learning paradigm that unifies ACD, ACSA, and RP into a coherent framework. To achieve this, we propose the Distantly Supervised Pyramid Network (DSPN), which employs a pyramid structure to capture sentiment at word, aspect, and document levels in a hierarchical manner. Evaluations on multi-aspect review datasets in English and Chinese show that DSPN, using only star rating labels for supervision, demonstrates significant efficiency advantages while performing comparably well to a variety of benchmark models. Additionally, DSPN's pyramid structure enables the interpretability of its outputs. Our findings validate DSPN's effectiveness and efficiency, establishing a robust, resource-efficient, unified framework for sentiment analysis.

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