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Exploiting Temporal Dynamics in Product Reviews for Dynamic Sentiment Prediction at the Aspect Level

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Guojun WangAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 15, Issue 4

Article No.: 68, Pages 1 - 29

https://doi.org/10.1145/3441451

Published: 18 April 2021 Publication History

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Abstract

Online reviews and ratings play an important role in shaping the purchase decisions of customers in e-commerce. Many researches have been done to make proper recommendations for users, by exploiting reviews, ratings, user profiles, or behaviors. However, the dynamic evolution of user preferences and item properties haven’t been fully exploited. Moreover, it lacks fine-grained studies at the aspect level. To address the above issues, we define two concepts of user maturity and item popularity, to better explore the dynamic changes for users and items. We strive to exploit fine-grained information at the aspect level and the evolution of users and items, for dynamic sentiment prediction. First, we analyze three real datasets from both the overall level and the aspect level, to discover the dynamic changes (i.e., gradual changes and sudden changes) in user aspect preferences and item aspect properties. Next, we propose a novel model of Aspect-based Sentiment Dynamic Prediction (ASDP), to dynamically capture and exploit the change patterns with uniform time intervals. We further propose the improved model ASDP+ with a bin segmentation algorithm to set the time intervals non-uniformly based on the sudden changes. Experimental results on three real-world datasets show that our work leads to significant improvements.

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Index Terms

Exploiting Temporal Dynamics in Product Reviews for Dynamic Sentiment Prediction at the Aspect Level
1. Information systems
  1. Information systems applications
    1. Data mining

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Information & Contributors

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 15, Issue 4

August 2021

486 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3458847

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

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Copyright © 2021 ACM.

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Publication History

Published: 18 April 2021

Accepted: 01 December 2020

Revised: 01 November 2020

Received: 01 February 2020

Published in TKDD Volume 15, Issue 4

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National Science Foundation
Guangdong Provincial NSF Grant
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National Science Foundation of China
the Science and Technology program of Changsha City

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Jin GLiang YFang YShao ZHuang JZhang JZheng Y(2024)Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.333382436:10(5388-5408)Online publication date: 1-Oct-2024
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