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Better Queries for Aspect-Category Sentiment Classification

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Abstract

Aspect-category sentiment classification (ACSC) aims to identify the sentiment polarities towards the aspect categories mentioned in a sentence. Because a sentence often mentions more than one aspect category and expresses different sentiment polarities to them, finding aspect category-related information from the sentence is the key challenge to accurately recognize the sentiment polarity. Most previous models take both sentence and aspect category as input and query aspect category-related information based on the aspect category. However, these models represent the aspect category as a context-independent vector called aspect embedding, which may not be effective enough as a query. In this paper, we propose two contextualized aspect category representations, Contextualized Aspect Vector (CAV) and Contextualized Aspect Matrix (CAM). Specifically, we use the coarse aspect category-related information found by the aspect category detection task to generate CAV or CAM. Then the CAV or CAM as queries are used to search for fine-grained aspect category-related information like aspect embedding by aspect-category sentiment classification models. In experiments, we integrate the proposed CAV and CAM into several representative aspect embedding-based aspect-category sentiment classification models. Experimental results on the SemEval-2014 Restaurant Review dataset and the Multi-Aspect Multi-Sentiment dataset demonstrate the effectiveness of CAV and CAM.

Y. Li, C. Yin—Equal contribution.

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Author information

Authors and Affiliations

  1. Baidu Inc., Beijing, China

    Yuncong Li, Cunxiang Yin, Huiqiang Zhong, Jinchang Luo, Siqi Xu & Xiaohui Wu

  2. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Sheng-hua Zhong

Authors
  1. Yuncong Li

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  2. Cunxiang Yin

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  3. Sheng-hua Zhong

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  4. Huiqiang Zhong

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  5. Jinchang Luo

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  6. Siqi Xu

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  7. Xiaohui Wu

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Corresponding author

Correspondence toSheng-hua Zhong.

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Maosong Sun

  2. Peking University, Beijing, China

    Sujian Li

  3. Westlake University, Hangzhou, China

    Yue Zhang

  4. Tsinghua University, Beijing, China

    Yang Liu

  5. Chinese Academy of Sciences, Beijing, China

    Shizhu He

  6. Beijing Language and Culture University, Beijing, China

    Gaoqi Rao

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Li, Y.et al. (2020). Better Queries for Aspect-Category Sentiment Classification. In: Sun, M., Li, S., Zhang, Y., Liu, Y., He, S., Rao, G. (eds) Chinese Computational Linguistics. CCL 2020. Lecture Notes in Computer Science(), vol 12522. Springer, Cham. https://doi.org/10.1007/978-3-030-63031-7_25

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