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What Is Your Cause for Concern? Towards Interpretable Complaint Cause Analysis

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Advances in Information Retrieval (ECIR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13981))

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Abstract

The abundance of information available on social media and the regularity with which complaints are posted online emphasizes the need for automated complaint analysis tools. Prior study has focused chiefly on complaint identification and complaint severity prediction: the former attempts to classify a piece of content as either complaint or non-complaint. The latter seeks to group complaints into various severity classes depending on the threat level that the complainant is prepared to accept. The complainant’s goal could be to express disapproval, seek compensation, or both. As a result, the complaint detection model should be interpretable or explainable. Recognizing the cause of a complaint in the text is a crucial yet untapped area of natural language processing research. We propose an interpretable complaint cause analysis model that is grounded on a dyadic attention mechanism. The model jointly learns complaint classification, emotion recognition, and polarity classification as the first sub-problem. Subsequently, the complaint cause extraction and the associated severity level prediction as the second sub-problem. We add the causal span annotation for the existing complaint classes in a publicly available complaint dataset to accomplish this. The results indicate that existing computational tools can be repurposed to tackle highly relevant novel tasks, thereby finding new research opportunities (Resources available at: https://bit.ly/Complaintcauseanalysis).

A. Singh and P. Jha—Denotes equal contribution.

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Notes

  1. 1.

    https://github.com/danielpreotiuc/complaints-social-media.

  2. 2.

    https://pytorch.org/.

  3. 3.

    https://www.tensorflow.org/.

  4. 4.

    https://scikit-learn.org/stable/.

  5. 5.

    https://huggingface.co/model and https://tfhub.dev/google/collections/bert/1.

  6. 6.

    For the significance test, we used the Student’s t-test (p-value < 0.04).

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Acknowledgement

This publication is an outcome of the R &D work undertaken in the project under the Visvesvaraya Ph.D. Scheme of Ministry of Electronics & Information Technology, Government of India, being implemented by Digital India Corporation (Formerly Media Lab Asia).

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Authors and Affiliations

  1. Indian Institute of Technology Patna, Bihta, India

    Apoorva Singh, Prince Jha & Sriparna Saha

  2. Delhi Technological University, Delhi, India

    Rohan Bhatia

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  1. Apoorva Singh
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  2. Prince Jha
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  3. Rohan Bhatia
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  4. Sriparna Saha
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Corresponding author

Correspondence to Apoorva Singh .

Editor information

Editors and Affiliations

  1. University of Amsterdam, Amsterdam, The Netherlands

    Jaap Kamps

  2. Université Grenoble-Alpes, Saint-Martin-d’Hères, France

    Lorraine Goeuriot

  3. Università della Svizzera Italiana, Lugano, Switzerland

    Fabio Crestani

  4. University of Copenhagen, Copenhagen, Denmark

    Maria Maistro

  5. University of Tsukuba, Ibaraki, Japan

    Hideo Joho

  6. Dublin City University, Dublin, Ireland

    Brian Davis

  7. Dublin City University, Dublin, Ireland

    Cathal Gurrin

  8. Universität Regensburg, Regensburg, Germany

    Udo Kruschwitz

  9. Dublin City University, Dublin, Ireland

    Annalina Caputo

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Singh, A., Jha, P., Bhatia, R., Saha, S. (2023). What Is Your Cause for Concern? Towards Interpretable Complaint Cause Analysis. In: Kamps, J., et al. Advances in Information Retrieval. ECIR 2023. Lecture Notes in Computer Science, vol 13981. Springer, Cham. https://doi.org/10.1007/978-3-031-28238-6_10

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  • DOI: https://doi.org/10.1007/978-3-031-28238-6_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28237-9

  • Online ISBN: 978-3-031-28238-6

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