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Contextualized Word Representations with Effective Attention for Aspect-Based Sentiment Analysis

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Part of the book series:Lecture Notes in Computer Science ((LNAI,volume 11856))

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Abstract

Aspect-based sentiment analysis (ABSA) aims at identifying sentiment polarities towards aspect in a sentence. Attention mechanism has played an important role in previous state-of-the-art neural models. However, existing attention mechanisms proposed for aspect based sentiment classification mostly focus on identifying the sentiment words, without considering the relevance of such words with respect to the given aspects in the sentence. To solve this problem, we propose a new architecture, self-attention with co-attention (SACA) for aspect-based sentiment analysis. Self-attention is capable of conducting direct connections between arbitrary two words in context from a global perspective, while co-attention can capture the word-level interaction between aspect and context. Moreover, previous works simply averaged aspect vector to learn the attention weights on the context words, which may bring information loss if the aspect has multiple words. To address the problem, we employ the pre-trained contextual word embeddings and character-level word embeddings as word representation. We evaluate the proposed approach on three datasets, experimental results demonstrate that our model outperforms the state-of-the-art on all three datasets.

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Acknowledgements

This work was supported by the Ministry of Education of Humanities and Social Science project (No. 19YJAZH128), and Guangdong Graduate Education Innovation project (No. 2018JGXM41).

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Authors and Affiliations

  1. School of Information Science and Technology, School of Cyber Security, Guangdong University of Foreign Studies, Guangzhou, China

    Zixuan Cao, Yongmei Zhou & Aimin Yang

  2. Eastern Language Processing Center, Guangdong University of Foreign Studies, Guangzhou, China

    Yongmei Zhou

  3. School of Business, Guangdong University of Foreign Studies, Guangzhou, China

    Aimin Yang & Jiahui Fu

Authors
  1. Zixuan Cao

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  2. Yongmei Zhou

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  3. Aimin Yang

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  4. Jiahui Fu

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Corresponding author

Correspondence toYongmei Zhou.

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Maosong Sun

  2. Fudan University, Shanghai, China

    Xuanjing Huang

  3. University of Illinois at Urbana Champaign, Illinois, USA

    Heng Ji

  4. Tsinghua University, Beijing, China

    Zhiyuan Liu

  5. Tsinghua University, Beijing, China

    Yang Liu

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Cao, Z., Zhou, Y., Yang, A., Fu, J. (2019). Contextualized Word Representations with Effective Attention for Aspect-Based Sentiment Analysis. In: Sun, M., Huang, X., Ji, H., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics. CCL 2019. Lecture Notes in Computer Science(), vol 11856. Springer, Cham. https://doi.org/10.1007/978-3-030-32381-3_38

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