research-article

Revised mutual information approach for german text sentiment classification

Authors:

Brigitte MathiakAuthors Info & Claims

WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide Web

Pages 579 - 586

https://doi.org/10.1145/2487788.2487997

Published: 13 May 2013 Publication History

Abstract

The significant increase in content of online social media such as product reviews, blogs, forums etc., have led to an increasing attention to sentiment analysis tools and approaches that make use of mining this substantially growing content. The aim of this paper is to develop a robust classification approach of customer reviews based on a self-annotated domain-specific corpus by applying a statistical approach i.e., mutual information. First, subjective words in each test sentence are identified. Second, ambiguous adjectives such as high, low, large, many etc., are disambiguated based on their accompanying noun using a conditional mutual information approach. Third, a mutual information approach is applied to find the sentiment orientation (polarity) of the identified subjective words based on analyzing their statistical relationship with the manually annotated sentiment labels within a sizeable sentiment training data. Fourth, since negation plays a significant role in flipping the sentiment polarity of an identified sentiment word, we estimate the role of negation in affecting the classification accuracy. Finally, the identified polarity for each test sentence is evaluated against experts' annotation.

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Zhang YZou YXie YChen L(2023)Identifying dynamic interaction patterns in mandatory and discretionary lane changes using graph structureComputer-Aided Civil and Infrastructure Engineering10.1111/mice.13099Online publication date: 23-Sep-2023
https://doi.org/10.1111/mice.13099
Mukhtar NAbid Khan MChiragh NNazir SUllah Jan A(2022)An Intelligent Unsupervised Approach for Handling Context-Dependent Words in Urdu Sentiment AnalysisACM Transactions on Asian and Low-Resource Language Information Processing10.1145/351083021:5(1-15)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3510830
Jin JLiu YJi PKwong C(2018)Review on Recent Advances in Information Mining From Big Consumer Opinion Data for Product DesignJournal of Computing and Information Science in Engineering10.1115/1.404108719:1Online publication date: 17-Sep-2018
https://doi.org/10.1115/1.4041087
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Index Terms

Revised mutual information approach for german text sentiment classification
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
    2. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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cover image ACM Other conferences

WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide Web

May 2013

1636 pages

ISBN:9781450320382

DOI:10.1145/2487788

General Chairs:
Daniel Schwabe
PUC-Rio - Brazil
,
Virgílio Almeida
UFMG - Brazil
,
Hartmut Glaser
CGI.br - Brazil
,
Program Chairs:
Ricardo Baeza-Yates
Yahoo! Labs - Spain & Chile
,
Sue Moon
KAIST - South Korea

Copyright © 2013 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

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NICBR: Nucleo de Informatcao e Coordenacao do Ponto BR
CGIBR: Comite Gestor da Internet no Brazil

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

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Published: 13 May 2013

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WWW '13: 22nd International World Wide Web Conference

May 13 - 17, 2013

Rio de Janeiro, Brazil

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WWW '13 Companion Paper Acceptance Rate 831 of 1,250 submissions, 66%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Zhang YZou YXie YChen L(2023)Identifying dynamic interaction patterns in mandatory and discretionary lane changes using graph structureComputer-Aided Civil and Infrastructure Engineering10.1111/mice.13099Online publication date: 23-Sep-2023
https://doi.org/10.1111/mice.13099
Mukhtar NAbid Khan MChiragh NNazir SUllah Jan A(2022)An Intelligent Unsupervised Approach for Handling Context-Dependent Words in Urdu Sentiment AnalysisACM Transactions on Asian and Low-Resource Language Information Processing10.1145/351083021:5(1-15)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3510830
Jin JLiu YJi PKwong C(2018)Review on Recent Advances in Information Mining From Big Consumer Opinion Data for Product DesignJournal of Computing and Information Science in Engineering10.1115/1.404108719:1Online publication date: 17-Sep-2018
https://doi.org/10.1115/1.4041087
Manjula SHegadi R(2017)Identification and classification of multilingual document using maximized mutual information2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS)10.1109/ICECDS.2017.8389734(1679-1682)Online publication date: Aug-2017
https://doi.org/10.1109/ICECDS.2017.8389734
Khan FQamar UBashir S(2016)SentiMIApplied Soft Computing10.1016/j.asoc.2015.11.01639:C(140-153)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1016/j.asoc.2015.11.016
Garg SSharma D(2015)Feature based clustering considering context dependent words2015 1st International Conference on Next Generation Computing Technologies (NGCT)10.1109/NGCT.2015.7375214(713-718)Online publication date: Sep-2015
https://doi.org/10.1109/NGCT.2015.7375214
Saad F(2014)Baseline evaluationProceedings of the 14th International Conference on Knowledge Technologies and Data-driven Business10.1145/2637748.2638420(1-8)Online publication date: 16-Sep-2014
https://dl.acm.org/doi/10.1145/2637748.2638420
Kansal HToshniwal D(2014)Aspect based Summarization of Context Dependent Opinion WordsProcedia Computer Science10.1016/j.procs.2014.08.09635(166-175)Online publication date: 2014
https://doi.org/10.1016/j.procs.2014.08.096

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