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A Contrastive Learning Framework with Tree-LSTMs for Aspect-Based Sentiment Analysis

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Abstract

Different from sentence-level sentiment analysis, the aspect-based sentiment analysis (ABSA) is a fine-grained task that aims to identify the sentiment polarity towards specific aspect terms in a sentence. However, the lack of fine-grained labeled data and the fact that a sentence may contain multiple aspects or complex implicit sentiment relations make ABSA still face challenges. Specifically, effectively exploiting syntactic dependencies to construct contextual information in a sentence to capture implicit sentiment polarities and constructing the data augmentation paradigm to obtain fine-grained aspect-specific information are the key concerns of this paper. To mitigate the above issues, we propose a Contrastive Learning Framework with Tree-Structured LSTM (CLF-TrLSTM), which applies a concatenated form of Tree-LSTMs and self-attention with window mechanism to utilize dependency tree to capture syntactic and contextual information of the sentence. Meanwhile, to alleviate the data scarcity problem, we use mask generation operation and contrastive learning to generate in-domain high-quality positive and negative samples, then encourage anchor sentences and positive samples to be more similar than negative example pairs, which can achieve alignment of different granularities. Finally, experimental results on three public datasets demonstrate that our proposed framework achieves the state-of-the-art performance and comprehensive analysis verifies the effectiveness of each component.

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Data Availability

Restaurant and Laptop datasets [3]: https://paperswithcode.com/dataset/semeval-2014-task-4-sub-task-2 Twitter dataset [18]: http://goo.gl/5Enpu7 Bert-base-uncased: https://github.com/huggingface/transformers

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Authors and Affiliations

  1. College of Computer Science and technology, Harbin Engineering University, No. 145 Nantong Street, Nangang District, Harbin, 150001, Heilongjiang, China

    Qichen Zhang, Shuai Wang & Jingmei Li

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  1. Qichen Zhang
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  2. Shuai Wang
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  3. Jingmei Li
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Correspondence to Qichen Zhang.

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No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part. All the authors listed have approved the manuscript that is enclosed.

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Zhang, Q., Wang, S. & Li, J. A Contrastive Learning Framework with Tree-LSTMs for Aspect-Based Sentiment Analysis. Neural Process Lett 55, 8869–8886 (2023). https://doi.org/10.1007/s11063-023-11181-9

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