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Modeling Multi-aspect Relationship with Joint Learning for Aspect-Level Sentiment Classification

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Database Systems for Advanced Applications (DASFAA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12112))

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Abstract

Aspect-level sentiment classification is a crucial branch for sentiment classification. Most of the existing work focuses on how to model the semantic relationship between the aspect and the sentence, while the relationships among the multiple aspects in the sentence is ignored. To address this problem, we propose a joint learning (Joint) model for aspect-level sentiment classification, which models the relationships among the aspects of the sentence and predicts the sentiment polarities of all aspects simultaneously. In particular, we first obtain the augmented aspect representation via an aspect modeling (AM) method. Then, we design a relationship modeling (RM) approach which transforms sentiment classification into a sequence labeling problem to model the potential relationships among each aspect in a sentence and predict the sentiment polarities of all aspects simultaneously. Extensive experiments on four benchmark datasets show that our approach can effectively improve the performance of aspect-level sentiment classification compared with the state-of-the-art approaches.

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Notes

  1. 1.

    Available at: http://alt.qcri.org/semeval2014/task4/.

  2. 2.

    Available at: http://alt.qcri.org/semeval2015/task12/.

  3. 3.

    Available at: http://alt.qcri.org/semeval2016/task5/.

  4. 4.

    https://pytorch.org/.

  5. 5.

    Available at: https://github.com/ruidan/Aspect-level-sentiment.

  6. 6.

    Available at: https://www.yelp.com/dataset/challenge.

  7. 7.

    Available at: http://jmcauley.ucsd.edu/data/amazon/.

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Acknowledgments

We greatly appreciate anonymous reviewers and the associate editor for their valuable and high quality comments that greatly helped to improve the quality of this article. This research is funded by the Science and Technology Commission of Shanghai Municipality (19511120200). This research is also supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada, an NSERC CREATE award in ADERSIM (http://www.yorku.ca/adersim), the York Research Chairs (YRC) program and an ORF-RE (Ontario Research Fund-Research Excellence) award in BRAIN Alliance (http://brainalliance.ca).

Author information

Authors and Affiliations

  1. Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, 200241, China

    Jie Zhou & Liang He

  2. School of Computer Science and Technology, East China Normal University, Shanghai, 200241, China

    Jie Zhou & Liang He

  3. Information Retrieval and Knowledge Management Research Lab, York University, Toronto, ON, M3J 1P3, Canada

    Jimmy Xiangji Huang

  4. The School of Computer Science, Ryerson University, Toronto, ON, M5B 2K3, Canada

    Qinmin Vivian Hu

Authors
  1. Jie Zhou
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  3. Qinmin Vivian Hu
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  4. Liang He
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Corresponding author

Correspondence to Jie Zhou .

Editor information

Editors and Affiliations

  1. Dankook University, Yongin, Korea (Republic of)

    Yunmook Nah

  2. Peking University, Haidian, China

    Bin Cui

  3. Sungkyunkwan University, Suwon, Korea (Republic of)

    Sang-Won Lee

  4. Department of System Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong, Hong Kong

    Jeffrey Xu Yu

  5. Kangwon National University, Chunchon, Korea (Republic of)

    Yang-Sae Moon

  6. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Steven Euijong Whang

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Zhou, J., Huang, J.X., Hu, Q.V., He, L. (2020). Modeling Multi-aspect Relationship with Joint Learning for Aspect-Level Sentiment Classification. In: Nah, Y., Cui, B., Lee, SW., Yu, J.X., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020. Lecture Notes in Computer Science(), vol 12112. Springer, Cham. https://doi.org/10.1007/978-3-030-59410-7_54

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