Document-Level Supervision for Multi-Aspect Sentiment Analysis Without Fine-grained Labels

• URL: http://arxiv.org/abs/2310.06940v1
• Date: Tue, 10 Oct 2023 18:53:21 GMT
• Title: Document-Level Supervision for Multi-Aspect Sentiment Analysis Without Fine-grained Labels
• Authors: Kasturi Bhattacharjee and Rashmi Gangadharaiah
• Abstract summary: We propose a VAE-based topic modeling approach that performs ABSA using document-level supervision. Our approach allows for the detection of multiple aspects in a document, thereby allowing for the possibility of reasoning about how sentiment expressed through multiple aspects comes together to form an observable document-level sentiment.
• Score: 14.187425779776204
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aspect-based sentiment analysis (ABSA) is a widely studied topic, most often trained through supervision from human annotations of opinionated texts. These fine-grained annotations include identifying aspects towards which a user expresses their sentiment, and their associated polarities (aspect-based sentiments). Such fine-grained annotations can be expensive and often infeasible to obtain in real-world settings. There is, however, an abundance of scenarios where user-generated text contains an overall sentiment, such as a rating of 1-5 in user reviews or user-generated feedback, which may be leveraged for this task. In this paper, we propose a VAE-based topic modeling approach that performs ABSA using document-level supervision and without requiring fine-grained labels for either aspects or sentiments. Our approach allows for the detection of multiple aspects in a document, thereby allowing for the possibility of reasoning about how sentiment expressed through multiple aspects comes together to form an observable overall document-level sentiment. We demonstrate results on two benchmark datasets from two different domains, significantly outperforming a state-of-the-art baseline.

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