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Thumbs up?: sentiment classification using machine learning techniques

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Shivakumar VaithyanathanAuthors Info & Claims

EMNLP '02: Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10

Pages 79 - 86

https://doi.org/10.3115/1118693.1118704

Published: 06 July 2002 Publication History

Abstract

We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we find that standard machine learning techniques definitively outperform human-produced baselines. However, the three machine learning methods we employed (Naive Bayes, maximum entropy classification, and support vector machines) do not perform as well on sentiment classification as on traditional topic-based categorization. We conclude by examining factors that make the sentiment classification problem more challenging.

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Cited By

Mamta Singh GKori DEkbal ACai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Aspect-Based Multimodal Mining: Unveiling Sentiments, Complaints, and Beyond in User-Generated ContentProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681703(6433-6442)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681703
Mamta Ekbal A(2024)Transformer based multilingual joint learning framework for code-mixed and english sentiment analysisJournal of Intelligent Information Systems10.1007/s10844-023-00808-x62:1(231-253)Online publication date: 1-Feb-2024
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Son YChoi AWowak KAngst C(2024)Gender mismatch and bias in people‐centric operationsJournal of Operations Management10.1002/joom.124970:5(E1-E17)Online publication date: 17-Jul-2024
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Thumbs up?: sentiment classification using machine learning techniques
1. Computing methodologies
  1. Artificial intelligence
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
2. Hardware
  1. Power and energy
    1. Power estimation and optimization

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EMNLP '02: Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10

July 2002

328 pages

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Association for Computational Linguistics

United States

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Published: 06 July 2002

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Cited By

Mamta Singh GKori DEkbal ACai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Aspect-Based Multimodal Mining: Unveiling Sentiments, Complaints, and Beyond in User-Generated ContentProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681703(6433-6442)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681703
Mamta Ekbal A(2024)Transformer based multilingual joint learning framework for code-mixed and english sentiment analysisJournal of Intelligent Information Systems10.1007/s10844-023-00808-x62:1(231-253)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1007/s10844-023-00808-x
Son YChoi AWowak KAngst C(2024)Gender mismatch and bias in people‐centric operationsJournal of Operations Management10.1002/joom.124970:5(E1-E17)Online publication date: 17-Jul-2024
https://dl.acm.org/doi/10.1002/joom.1249
Dhurandhar ARamamurthy KAhuja KArya VOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Locally invariant explanationsProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3666975(19410-19445)Online publication date: 10-Dec-2023
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Bhatia KNarayan ADe Sa CRé COh ANaumann TGloberson ASaenko KHardt MLevine S(2023)TARTProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3666549(9751-9788)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3666549
Ali NTubaishat AAl-Obeidat FShabaz MWaqas MHalim ZRida IAnwar S(2023)Towards Enhanced Identification of Emotion from Resource-Constrained Language through a novel Multilingual BERT ApproachACM Transactions on Asian and Low-Resource Language Information Processing10.1145/3592794Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3592794
Das RSingh T(2023)Multimodal Sentiment Analysis: A Survey of Methods, Trends, and ChallengesACM Computing Surveys10.1145/358607555:13s(1-38)Online publication date: 13-Jul-2023
https://dl.acm.org/doi/10.1145/3586075
Das RSingh T(2023)Image–Text Multimodal Sentiment Analysis Framework of Assamese News Articles Using Late FusionACM Transactions on Asian and Low-Resource Language Information Processing10.1145/358486122:6(1-30)Online publication date: 17-Feb-2023
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Cunha WViegas FFrança CRosa TRocha LGonçalves M(2023)A Comparative Survey of Instance Selection Methods applied to Non-Neural and Transformer-Based Text ClassificationACM Computing Surveys10.1145/358200055:13s(1-52)Online publication date: 13-Jul-2023
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Chen JYao ZZhao SZhang Y(2023)Fusion Pre-trained Emoji Feature Enhancement for Sentiment AnalysisACM Transactions on Asian and Low-Resource Language Information Processing10.1145/357858222:4(1-14)Online publication date: 25-Mar-2023
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