research-article

Adapting sentiment lexicons to domain-specific social media texts

Authors:

Atish P. Sinha,

Huimin ZhaoAuthors Info & Claims

Decision Support Systems, Volume 94, Issue C

Pages 65 - 76

https://doi.org/10.1016/j.dss.2016.11.001

Published: 01 February 2017 Publication History

Abstract

Social media has become the largest data source of public opinion. The application of sentiment analysis to social media texts has great potential, but faces great challenges because of domain heterogeneity. Sentiment orientation of words varies by content domain, but learning context-specific sentiment in social media domains continues to be a major challenge. The language domain poses another challenge since the language used in social media today differs significantly from that used in traditional media. To address these challenges, we propose a method to adapt existing sentiment lexicons for domain-specific sentiment classification using an unannotated corpus and a dictionary. We evaluate our method using two large developing corpora, containing 743,069 tweets related to the stock market and one million tweets related to political topics, respectively, and five existing sentiment lexicons as seeds and baselines. The results demonstrate the usefulness of our method, showing significant improvement in sentiment classification performance. We propose a method to adapt existing sentiment lexicons for domain-specific sentiment classification.The proposed method addresses challenges from both content domain and language domain.We evaluate our method using two large developing corpora and five existing sentiment lexicons as seeds and baselines.The evaluation results demonstrate the usefulness of our method.

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cover image Decision Support Systems

Decision Support Systems Volume 94, Issue C

February 2017

109 pages

ISSN:0167-9236

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Copyright © Elsevier B.V.

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Elsevier Science Publishers B. V.

Netherlands

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Published: 01 February 2017

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Cited By

Dong LZheng HLi LZhou C(2024)Distilling wisdom of crowds in online communitiesDecision Support Systems10.1016/j.dss.2024.114190180:COnline publication date: 9-Jul-2024
https://dl.acm.org/doi/10.1016/j.dss.2024.114190
Pattusamy MKanth L(2023)Classification of Tweets Into Facts and Opinions Using Recurrent Neural NetworksInternational Journal of Technology and Human Interaction10.4018/IJTHI.31935819:1(1-14)Online publication date: 10-Mar-2023
https://dl.acm.org/doi/10.4018/IJTHI.319358
Ameur AHamdi SBen Yahia S(2023)Sentiment Analysis for Hotel Reviews: A Systematic Literature ReviewACM Computing Surveys10.1145/360515256:2(1-38)Online publication date: 15-Sep-2023
https://dl.acm.org/doi/10.1145/3605152
Daradkeh M(2022)Organizational Adoption of Sentiment Analytics in Social Media NetworksInternational Journal of Information Technologies and Systems Approach10.4018/IJITSA.30702315:2(1-29)Online publication date: 10-Aug-2022
https://dl.acm.org/doi/10.4018/IJITSA.307023
Xie ZLiu GQu JWu JLi H(2022)Identifying Structural Holes for Sentiment ClassificationInformation Systems Frontiers10.1007/s10796-021-10185-x24:5(1735-1751)Online publication date: 1-Oct-2022
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Lee GKim CSong M(2021)Semisupervised sentiment analysis method for online text reviewsJournal of Information Science10.1177/016555152091003247:3(387-403)Online publication date: 1-Jun-2021
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Du MLi XLuo L(2021)A Training-Optimization-Based Method for Constructing Domain-Specific Sentiment LexiconComplexity10.1155/2021/61524942021Online publication date: 1-Jan-2021
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de Melo TPereira Ada Rocha L(2021)Building a Restaurant-Specific Sentiment Lexicon via Probability TheoryProceedings of the Brazilian Symposium on Multimedia and the Web10.1145/3470482.3479453(129-132)Online publication date: 5-Nov-2021
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Naresh Kumar KUma V(2021)Intelligent sentinet-based lexicon for context-aware sentiment analysis: optimized neural network for sentiment classification on social mediaThe Journal of Supercomputing10.1007/s11227-021-03709-477:11(12801-12825)Online publication date: 13-Apr-2021
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Ye WLee A(2021)Mining sentiment tendencies and summaries from consumer reviewsInformation Systems and e-Business Management10.1007/s10257-020-00482-419:1(107-135)Online publication date: 1-Mar-2021
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