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Improved Representations for Personalized Document-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

Incorporating personalization into document-level sentiment classification has gained considerable attention due to its better performance on diverse domains. Current progress in this field is attributed to the developed mechanisms of effectively modeling the interaction among the three fundamental factors: users, items, and words. However, how to improve the representation learning of the three factors themselves is largely unexplored. To bridge this gap, we propose to enrich users, items, and words representations in the state-of-the-art personalized sentiment classification model with an end-to-end training fashion. Specifically, relations between users and items are respectively modeled by graph neural networks to enhance original user and item representations. We further promote word representation by utilizing powerful pre-trained language models. Comprehensive experiments on several public and widely-used datasets demonstrate the superiority of the proposed approach, validating the contribution of the improved representations.

This work was supported in part by the National Key Research and Development Program (2019YFB2102600), NSFC (61702190), Shanghai Sailing Program (17YF1404500), and Zhejiang Lab (2019KB0AB04).

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Notes

  1. 1.

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

  2. 2.

    http://www.nltk.org/.

  3. 3.

    https://github.com/thunlp/NSC.

  4. 4.

    https://github.com/wuzhen247/HUAPA.

  5. 5.

    http://emmanicholsromance.com/.

  6. 6.

    http://www.annalowebooks.com/.

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Authors and Affiliations

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

    Yihong Zhang & Wei Zhang

Authors
  1. Yihong Zhang
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  2. Wei Zhang
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Corresponding author

Correspondence to Wei Zhang .

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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Zhang, Y., Zhang, W. (2020). Improved Representations for Personalized Document-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_53

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