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Document-Level Multi-Aspect Sentiment Classification for Online Reviews of Medical Experts

Authors:

Vineeth Rakesh,

Chandan K. ReddyAuthors Info & Claims

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

Pages 2723 - 2731

https://doi.org/10.1145/3357384.3357828

Published: 03 November 2019 Publication History

Abstract

In the era of big data, online doctor review platforms, which enable patients to give feedback to their doctors, have become one of the most important components in healthcare systems. On one hand, they help patients to choose their doctors based on the experience of others. On the other hand, they help doctors to improve the quality of their service. Moreover, they provide important sources for us to discover common concerns of patients and existing problems in clinics, which potentially improve current healthcare systems. In this paper, we systematically investigate the dataset from one of such review platform, namely, ratemds.com, where each review for a doctor comes with an overall rating and ratings of four different aspects. A comprehensive statistical analysis is conducted first for reviews, ratings, and doctors. Then, we explore the content of reviews by extracting latent topics related to different aspects with unsupervised topic modeling techniques. As the core component of this paper, we propose a multi-task learning framework for the document-level multi-aspect sentiment classification. This task helps us to not only recover missing aspect-level ratings and detect inconsistent rating scores but also identify aspect-keywords for a given review based on ratings. The proposed model takes both features of doctors and aspect-keywords into consideration. Extensive experiments have been conducted on two subsets of ratemds dataset to demonstrate the effectiveness of the proposed model.

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Cited By

Li SLi YZhao WDing BWen J(2021)Interpretable Aspect-Aware Capsule Network for Peer Review Based Citation Count PredictionACM Transactions on Information Systems10.1145/346664040:1(1-29)Online publication date: 24-Nov-2021
https://dl.acm.org/doi/10.1145/3466640
Wu ZGao JLi QGuan ZChen Z(2021)Make aspect-based sentiment classification go further: step into the long-document-levelApplied Intelligence10.1007/s10489-021-02836-y52:8(8428-8447)Online publication date: 29-Oct-2021
https://doi.org/10.1007/s10489-021-02836-y

Index Terms

Document-Level Multi-Aspect Sentiment Classification for Online Reviews of Medical Experts

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cover image ACM Conferences

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

November 2019

3373 pages

ISBN:9781450369763

DOI:10.1145/3357384

General Chairs:
Wenwu Zhu
Tsinghua University, China
,
Dacheng Tao
University of Massachusetts, USA
,
Xueqi Cheng
Institute of Computing Technology, CAS, China
,
Program Chairs:
Peng Cui
Tsinghua University, China
,
Elke Rundensteiner
Worcester Polytechnic Institute, USA
,
David Carmel
Amazon Research, USA
,
Qi He
LinkedIn, USA
,
Jeffrey Xu Yu
Chinese University of Hong Kong, China

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Published: 03 November 2019

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CIKM '19: The 28th ACM International Conference on Information and Knowledge Management

November 3 - 7, 2019

Beijing, China

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CIKM '19 Paper Acceptance Rate 202 of 1,031 submissions, 20%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Li SLi YZhao WDing BWen J(2021)Interpretable Aspect-Aware Capsule Network for Peer Review Based Citation Count PredictionACM Transactions on Information Systems10.1145/346664040:1(1-29)Online publication date: 24-Nov-2021
https://dl.acm.org/doi/10.1145/3466640
Wu ZGao JLi QGuan ZChen Z(2021)Make aspect-based sentiment classification go further: step into the long-document-levelApplied Intelligence10.1007/s10489-021-02836-y52:8(8428-8447)Online publication date: 29-Oct-2021
https://doi.org/10.1007/s10489-021-02836-y

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