article

Joint model for subsentence-level sentiment analysis with Markov logic

Authors:

Tinglei HuangAuthors Info & Claims

Journal of the Association for Information Science and Technology, Volume 66, Issue 9

Pages 1913 - 1922

https://doi.org/10.1002/asi.23301

Published: 01 September 2015 Publication History

Abstract

Sentiment analysis mainly focuses on the study of one's opinions that express positive or negative sentiments. With the explosive growth of web documents, sentiment analysis is becoming a hot topic in both academic research and system design. Fine-grained sentiment analysis is traditionally solved as a 2-step strategy, which results in cascade errors. Although joint models, such as joint sentiment/topic and maximum entropy MaxEnt/latent Dirichlet allocation, are proposed to tackle this problem of sentiment analysis, they focus on the joint learning of both aspects and sentiments. Thus, they are not appropriate to solve the cascade errors for sentiment analysis at the sentence or subsentence level. In this article, we present a novel jointly fine-grained sentiment analysis framework at the subsentence level with Markov logic. First, we divide the task into 2 separate stages subjectivity classification and polarity classification. Then, the 2 separate stages are processed, respectively, with different feature sets, which are implemented by local formulas in Markov logic. Finally, global formulas in Markov logic are adopted to realize the interactions of the 2 separate stages. The joint inference of subjectivity and polarity helps prevent cascade errors. Experiments on a Chinese sentiment data set manifest that our joint model brings significant improvements.

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Cited By

Wang ZHe SXu GRen M(2024)Will sentiment analysis need subculture? A new data augmentation approachJournal of the Association for Information Science and Technology10.1002/asi.2487275:6(655-670)Online publication date: 12-May-2024
https://dl.acm.org/doi/10.1002/asi.24872
Onan ABulut HKorukoglu S(2017)An improved ant algorithm with LDA-based representation for text document clusteringJournal of Information Science10.1177/016555151663878443:2(275-292)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1177/0165551516638784

Joint model for subsentence-level sentiment analysis with Markov logic
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cover image Journal of the Association for Information Science and Technology

Journal of the Association for Information Science and Technology Volume 66, Issue 9

September 2015

218 pages

ISSN:2330-1635

EISSN:2330-1643

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John Wiley & Sons, Inc.

United States

Publication History

Published: 01 September 2015

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Cited By

Wang ZHe SXu GRen M(2024)Will sentiment analysis need subculture? A new data augmentation approachJournal of the Association for Information Science and Technology10.1002/asi.2487275:6(655-670)Online publication date: 12-May-2024
https://dl.acm.org/doi/10.1002/asi.24872
Onan ABulut HKorukoglu S(2017)An improved ant algorithm with LDA-based representation for text document clusteringJournal of Information Science10.1177/016555151663878443:2(275-292)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1177/0165551516638784

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