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Article

Determining the semantic orientation of terms through gloss classification

Authors:

Fabrizio SebastianiAuthors Info & Claims

CIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management

Pages 617 - 624

https://doi.org/10.1145/1099554.1099713

Published: 31 October 2005 Publication History

Abstract

Sentiment classification is a recent subdiscipline of text classification which is concerned not with the topic a document is about, but with the opinion it expresses. It has a rich set of applications, ranging from tracking users' opinions about products or about political candidates as expressed in online forums, to customer relationship management. Functional to the extraction of opinions from text is the determination of the orientation of ``subjective'' terms contained in text, i.e. the determination of whether a term that carries opinionated content has a positive or a negative connotation. In this paper we present a new method for determining the orientation of subjective terms. The method is based on the quantitative analysis of the glosses of such terms, i.e. the definitions that these terms are given in on-line dictionaries, and on the use of the resulting term representations for semi-supervised term classification. The method we present outperforms all known methods when tested on the recognized standard benchmarks for this task.

References

[1]

S. R. Das and M. Y. Chen. Yahoo! for Amazon: Sentiment parsing from small talk on the Web. In Proceedings of the 8th Asia Pacific Finance Association Annual Conference, Barcelona, ES, 2001.

[2]

K. Dave, S. Lawrence, and D. M. Pennock. Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of WWW-03, 12th International Conference on the World Wide Web, pages 519--528, Budapest, HU, 2003. ACM Press, New York, US.

Digital Library

[3]

S. D. Durbin, J. N. Richter, and D. Warner. A system for affective rating of texts. In Proceedings of OTC-03, 3rd Workshop on Operational Text Classification, Washington, US, 2003.

[4]

Z. Fei, J. Liu, and G. Wu. Sentiment classification using phrase patterns. In Proceedings of CIT-04, 4th International Conference on Computer and Information Technology, pages 1147--1152, Wuhan, CN, 2004.

Digital Library

[5]

G. Grefenstette, Y. Qu, J. G. Shanahan, and D. A. Evans. Coupling niche browsers and affect analysis for an opinion mining application. In Proceedings of RIAO-04, 7th International Conference on "Recherche d'Information Assistée par Ordinateur", pages 186--194, Avignon, FR, 2004.

[6]

V. Hatzivassiloglou and K. R. McKeown. Predicting the semantic orientation of adjectives. In Proceedings of ACL-97, 35th Annual Meeting of the Association for Computational Linguistics, pages 174--181, Madrid, ES, 1997. Association for Computational Linguistics.

Digital Library

[7]

V. Hatzivassiloglou and J. M. Wiebe. Effects of adjective orientation and gradability on sentence subjectivity. In Proceedings of COLING-00, 18th International Conference on Computational Linguistics, pages 174--181, 2000.

Digital Library

[8]

T. Joachims. A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization. In D. H. Fisher, editor, Proceedings of ICML-97, 14th International Conference on Machine Learning, pages 143--151, Nashville, US, 1997. Morgan Kaufmann Publishers, San Francisco, US.

[9]

J. Kamps, M. Marx, R. J. Mokken, and M. D. Rijke. Using WordNet to measure semantic orientation of adjectives. In Proceedings of LREC-04, 4th International Conference on Language Resources and Evaluation, volume IV, pages 1115--1118, Lisbon, PT, 2004.

[10]

S.-M. Kim and E. Hovy. Determining the sentiment of opinions. In Proceedings of COLING-04, 20th International Conference on Computational Linguistics, pages 1367--1373, Geneva, CH, 2004.

Digital Library

[11]

S. Morinaga, K. Yamanishi, K. Tateishi, and T. Fukushima. Mining product reputations on the Web. In Proceedings of KDD-02, 8th ACM International Conference on Knowledge Discovery and Data Mining, pages 341--349, Edmonton, CA, 2002. ACM Press.S. Morinaga, K. Yamanishi, K. Tateishi, and T. Fukushima. Mining product reputations on the Web. In Proceedings of KDD-02, 8th ACM International Conference on Knowledge Discovery and Data Mining, pages 341--349, Edmonton, CA, 2002. ACM Press.

[12]

T. Nasukawa and J. Yi. Sentiment analysis: Capturing favorability using natural language processing. In Proceedings of the K-CAP-03, 2nd International Conference on Knowledge Capture, pages 70--77, New York, US, 2003. ACM Press.

Digital Library

[13]

B. Pang and L. Lee. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of ACL-04, 42nd Meeting of the Association for Computational Linguistics, pages 271--278, Barcelona, ES, 2004.

Digital Library

[14]

B. Pang, L. Lee, and S. Vaithyanathan. Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of EMNLP-02, 7th Conference on Empirical Methods in Natural Language Processing, pages 79--86, Philadelphia, US, 2002. Association for Computational Linguistics, Morristown, US.

Digital Library

[15]

E. Riloff, J. Wiebe, and T. Wilson. Learning subjective nouns using extraction pattern bootstrapping. In W. Daelemans and M. Osborne, editors, Proceedings of CONLL-03, 7th Conference on Natural Language Learning, pages 25--32, Edmonton, CA, 2003.

Digital Library

[16]

P. J. Stone, D. C. Dunphy, M. S. Smith, and D. M. Ogilvie. The General Inquirer: A Computer Approach to Content Analysis. MIT Press, Cambridge, US, 1966.

[17]

P. Turney. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proceedings of ACL-02, 40th Annual Meeting of the Association for Computational Linguistics, pages 417--424, 2002.

Digital Library

[18]

P. D. Turney and M. L. Littman. Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems, 21(4):315--346, 2003.

Digital Library

[19]

T. Wilson, J. Wiebe, and R. Hwa. Just how mad are you? Finding strong and weak opinion clauses. In Proceedings of AAAI-04, 21st Conference of the American Association for Artificial Intelligence, San Jose, US, 2004.

Digital Library

[20]

H. Yu and V. Hatzivassiloglou. Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences. In M. Collins and M. Steedman, editors, Proceedings of EMNLP-03, 8th Conference on Empirical Methods in Natural Language Processing, pages 129--136, 2003.

Digital Library

Cited By

Janardana Naidu GSeshashayee M(2024)Sentiment Analysis Framework for Telugu Text Based on Novel Contrived Passive Aggressive with Fuzzy Weighting Classifier (CPSC-FWC)Система анализа тональности текста на телугу на основе нового пассивно-агрессивного классификатора с нечетким взвешиваниемInformatics and AutomationИнформатика и автоматизация10.15622/ia.23.1.223:1(39-64)Online publication date: 11-Jan-2024
https://doi.org/10.15622/ia.23.1.2
Ghafoor AImran ADaudpota SKastrati ZShaikh SBatra R(2023)SentiUrdu-1M: A large-scale tweet dataset for Urdu text sentiment analysis using weakly supervised learningPLOS ONE10.1371/journal.pone.029077918:8(e0290779)Online publication date: 30-Aug-2023
https://doi.org/10.1371/journal.pone.0290779
Wiegand MSchulder MRuppenhofer J(2023)Determining sentiment views of verbal multiword expressions using linguistic featuresNatural Language Engineering10.1017/S1351324923000153(1-38)Online publication date: 15-May-2023
https://doi.org/10.1017/S1351324923000153
Show More Cited By

Index Terms

Determining the semantic orientation of terms through gloss classification
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees
2. Information systems
  1. Information retrieval

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Published In

cover image ACM Conferences

CIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management

October 2005

854 pages

ISBN:1595931406

DOI:10.1145/1099554

General Chair:
Otthein Herzog
University of Bremen, Germany
,
Program Chairs:
Hans-Jörg Schek
University for Health Sciences, Medical Informatics and Technology, Austria
,
Norbert Fuhr
University of Duisburg-Essen, Germany
,
Abdur Chowdhury
America Online, USA
,
Wilfried Teiken
IBM T.J. Watson Research Center, USA

Copyright © 2005 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 31 October 2005

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CIKM05

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CIKM05: Conference on Information and Knowledge Management

October 31 - November 5, 2005

Bremen, Germany

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CIKM '05 Paper Acceptance Rate 77 of 425 submissions, 18%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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CIKM '25

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The 34th ACM International Conference on Information and Knowledge Management

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Cited By

Janardana Naidu GSeshashayee M(2024)Sentiment Analysis Framework for Telugu Text Based on Novel Contrived Passive Aggressive with Fuzzy Weighting Classifier (CPSC-FWC)Система анализа тональности текста на телугу на основе нового пассивно-агрессивного классификатора с нечетким взвешиваниемInformatics and AutomationИнформатика и автоматизация10.15622/ia.23.1.223:1(39-64)Online publication date: 11-Jan-2024
https://doi.org/10.15622/ia.23.1.2
Ghafoor AImran ADaudpota SKastrati ZShaikh SBatra R(2023)SentiUrdu-1M: A large-scale tweet dataset for Urdu text sentiment analysis using weakly supervised learningPLOS ONE10.1371/journal.pone.029077918:8(e0290779)Online publication date: 30-Aug-2023
https://doi.org/10.1371/journal.pone.0290779
Wiegand MSchulder MRuppenhofer J(2023)Determining sentiment views of verbal multiword expressions using linguistic featuresNatural Language Engineering10.1017/S1351324923000153(1-38)Online publication date: 15-May-2023
https://doi.org/10.1017/S1351324923000153
Pelosi SMaisto AMelillo LElia A(2023)Sentiment Analysis Through Finite State AutomataComputational Linguistics and Intelligent Text Processing10.1007/978-3-031-24340-0_14(182-197)Online publication date: 26-Feb-2023
https://doi.org/10.1007/978-3-031-24340-0_14
Lu XGu DZhang HSong ZCai QZhao HWu H(2022)Semi-Supervised Sentiment Classification on E-Commerce Reviews Using Tripartite Graph and ClusteringInternational Journal of Data Warehousing and Mining10.4018/IJDWM.30790418:1(1-20)Online publication date: 1-Jan-2022
https://doi.org/10.4018/IJDWM.307904
Catelli RPelosi SEsposito M(2022)Lexicon-Based vs. Bert-Based Sentiment Analysis: A Comparative Study in ItalianElectronics10.3390/electronics1103037411:3(374)Online publication date: 26-Jan-2022
https://doi.org/10.3390/electronics11030374
Kathuria ASharma A(2022)Sentiment Analysis Using Learning TechniquesMobile Radio Communications and 5G Networks10.1007/978-981-16-7018-3_42(559-581)Online publication date: 3-Mar-2022
https://doi.org/10.1007/978-981-16-7018-3_42
Aggarwal CAggarwal C(2022)Opinion Mining and Sentiment AnalysisMachine Learning for Text10.1007/978-3-030-96623-2_15(491-514)Online publication date: 10-Feb-2022
https://doi.org/10.1007/978-3-030-96623-2_15
Dubey GSharma P(2022)A Neural Network Based Approach for Text-Level Sentiment Analysis Using Sentiment LexiconsArtificial Intelligence and Speech Technology10.1007/978-3-030-95711-7_12(134-150)Online publication date: 29-Jan-2022
https://doi.org/10.1007/978-3-030-95711-7_12
Kumar AJain R(2022)Uniform Textual Feedback Analysis for Effective Sentiment AnalysisKnowledge Graphs and Semantic Web10.1007/978-3-030-91305-2_21(273-289)Online publication date: 1-Jan-2022
https://doi.org/10.1007/978-3-030-91305-2_21
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