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Multi-Feature and Multi-Channel GCNs for Aspect Based Sentiment Analysis

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Database and Expert Systems Applications (DEXA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14147))

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Abstract

Aspect Based Sentiment Analysis (ABSA) is a challenging task in natural language processing that involves identifying the sentiment polarity of different aspects in a given text. While graph convolutional network (GCN) has been shown to achieve state-of-the-art results for ABSA, existing methods typically rely on investigating a single type of feature in the sentence to construct the graph representation, which may not capture enough relevant information. In this paper, we propose an approach that leverages multiple channels to extract multiple features, including aspect relations, word dependency relation types, and semantic information, to enhance the performance of the model. We evaluate our approach on four benchmark datasets and demonstrate its validity, achieving state-of-the-art results. We also conduct extensive ablation studies to analyze the contribution of different components of our model. Our findings suggest that the combination of multiple channels and multiple features GCN is crucial for achieving the best performance in ABSA tasks. In conclusion, our proposed approach provides a promising solution to the ABSA problem and contributes to advancing the field by highlighting the importance of considering multiple features and leveraging the power of multiple channels GCN to improve performance.

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Notes

  1. 1.

    https://github.com/xijuan-hdu/MFMCGCN.

  2. 2.

    https://github.com/stanfordnlp/GloVe.

  3. 3.

    https://github.com/huggingface/transformers.

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Acknowledgements

This work is supported by the Kajima Foundation’s Support Program for International Joint Research Activities and JSPS KAKENHI (No.22K12146, 23H03402).

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Authors and Affiliations

  1. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China

    Wenlong Xi & Xiaoxi Huang

  2. Graduate Faculty of Interdisciplinary Research, University of Yamanashi, Kofu, Japan

    Fumiyo Fukumoto & Yoshimi Suzuki

Authors
  1. Wenlong Xi
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  2. Xiaoxi Huang
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  3. Fumiyo Fukumoto
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  4. Yoshimi Suzuki
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Corresponding author

Correspondence to Yoshimi Suzuki .

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Editors and Affiliations

  1. University of Vienna, Vienna, Austria

    Christine Strauss

  2. University of Tsukuba, Ibaraki, Japan

    Toshiyuki Amagasa

  3. Johannes Kepler University Linz, Linz, Austria

    Gabriele Kotsis

  4. Vienna University of Technology, Vienna, Austria

    A Min Tjoa

  5. Johannes Kepler University Linz, Linz, Austria

    Ismail Khalil

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Xi, W., Huang, X., Fukumoto, F., Suzuki, Y. (2023). Multi-Feature and Multi-Channel GCNs for Aspect Based Sentiment Analysis. In: Strauss, C., Amagasa, T., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2023. Lecture Notes in Computer Science, vol 14147. Springer, Cham. https://doi.org/10.1007/978-3-031-39821-6_13

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  • DOI: https://doi.org/10.1007/978-3-031-39821-6_13

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