Article

"I Know What You Feel": Analyzing the Role of Conjunctions in Automatic Sentiment Analysis

Authors:

Ritesh Agarwal,

T. V. Prabhakar,

Sugato ChakrabartyAuthors Info & Claims

GoTAL '08: Proceedings of the 6th international conference on Advances in Natural Language Processing

Pages 28 - 39

https://doi.org/10.1007/978-3-540-85287-2_4

Published: 25 August 2008 Publication History

Abstract

We are interested in finding how people feel about certain topics. This could be considered as a task of classifying the <em>sentiment</em>: sentiment could be positive, negative or neutral. In this paper, we examine the problem of automatic sentiment analysis at <em>sentence level</em>. We observe that sentence structure has a fair contribution towards sentiment determination, and conjunctions play a major role in defining the sentence structure. Our assumption is that in presence of conjunctions, not all phrases have equal contribution towards overall sentiment. We compile a set of conjunction rules to determine relevant phrases for sentiment analysis. Our approach is a representation of the idea to use linguistic resources at phrase level for the analysis at sentence level. We incorporate our approach with support vector machines to conclude that linguistic analysis plays a significant role in sentiment determination. Finally, we verify our results on movie, car and book reviews.

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Cited By

Gupta SBouadjenek MRobles-Kelly ALee TNguyen TNazari AThiruvady DSerra ESpezzano F(2024)Covid19-twitter: A Twitter-based Dataset for Discourse Analysis in Sentence-level Sentiment ClassificationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679120(5370-5374)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679120
Gupta SBouadjenek MRobles-Kelly A(2023)PERCYKnowledge-Based Systems10.1016/j.knosys.2023.110685275:COnline publication date: 5-Sep-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110685
Gupta SBouadjenek MRobles-Kelly A(2023)A Mask-Based Logic Rules Dissemination Method for Sentiment ClassifiersAdvances in Information Retrieval10.1007/978-3-031-28244-7_25(394-408)Online publication date: 2-Apr-2023
https://dl.acm.org/doi/10.1007/978-3-031-28244-7_25
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"I Know What You Feel": Analyzing the Role of Conjunctions in Automatic Sentiment Analysis
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Hardware
  1. Power and energy
    1. Power estimation and optimization

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Information

Published In

cover image Guide Proceedings

GoTAL '08: Proceedings of the 6th international conference on Advances in Natural Language Processing

August 2008

509 pages

ISBN:9783540852865

Editors:
Bengt Nordström
Department of Computer Science and Engineering, Chalmers University of Technology, Göteborg, Sweden 41296
,
Aarne Ranta
Department of Computer Science and Engineering, Chalmers University of Technology, Göteborg, Sweden 41296

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 25 August 2008

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Cited By

Gupta SBouadjenek MRobles-Kelly ALee TNguyen TNazari AThiruvady DSerra ESpezzano F(2024)Covid19-twitter: A Twitter-based Dataset for Discourse Analysis in Sentence-level Sentiment ClassificationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679120(5370-5374)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679120
Gupta SBouadjenek MRobles-Kelly A(2023)PERCYKnowledge-Based Systems10.1016/j.knosys.2023.110685275:COnline publication date: 5-Sep-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110685
Gupta SBouadjenek MRobles-Kelly A(2023)A Mask-Based Logic Rules Dissemination Method for Sentiment ClassifiersAdvances in Information Retrieval10.1007/978-3-031-28244-7_25(394-408)Online publication date: 2-Apr-2023
https://dl.acm.org/doi/10.1007/978-3-031-28244-7_25
Ma BYuan HWu Y(2017)Exploring performance of clustering methods on document sentiment analysisJournal of Information Science10.1177/016555151561737443:1(54-74)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1177/0165551515617374

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