skip to main content
10.1145/1390334.1390481acmconferencesArticle/Chapter ViewAbstractPublication PagesirConference Proceedingsconference-collections
poster

Combining learn-based and lexicon-based techniques for sentiment detection without using labeled examples

Published: 20 July 2008 Publication History

Abstract

In this work, we propose a novel scheme for sentiment classification (without labeled examples) which combines the strengths of both "learn-based" and "lexicon-based" approaches as follows: we first use a lexicon-based technique to label a portion of informative examples from given task (or domain); then learn a new supervised classifier based on these labeled ones; finally apply this classifier to the task. The experimental results indicate that proposed scheme could dramatically outperform "learn-based" and "lexicon-based" techniques.

References

[1]
B. Pang, L. Lee, et al. Thumbs up? Sentiment classification using machine learning techniques. EMNLP, 2002.]]
[2]
A. Aue and M. Gamon. Customizing Sentiment Classifiers to New Domains: a Case Study. RANLP. 2005.]]
[3]
P. D. Turney. Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. ACL, 2002.]]
[4]
P Philip J. Stone, Dexter C. Dunphy, et al. The General Inquirer: A Computer Approach to Content Analysis. The MIT Press, 1966.]]
[5]
L. Ku, Y. Liang, et al. Opinion Extraction, Summarization and Tracking in News and Blog Corpora. AAAI-2006 Spring Symposium on Computational Approaches to Analyzing Weblogs.]]
[6]
E. Han and G. Karypis. Centroid-Based Document Classification Analysis & Experimental Result. PKDD 2000.]]

Cited By

View all
  • (2022)A Survey on Sentiment Analysis Techniques for TwitterData Mining Approaches for Big Data and Sentiment Analysis in Social Media10.4018/978-1-7998-8413-2.ch003(57-90)Online publication date: 2022
  • (2021)SSentiA: A Self-supervised Sentiment Analyzer for classification from unlabeled dataMachine Learning with Applications10.1016/j.mlwa.2021.100026(100026)Online publication date: Mar-2021
  • (2020)A mixed approach of statistical weighting method and unsupervised method to improve Uyghur sentiment classificationJournal of Computational Methods in Sciences and Engineering10.3233/JCM-204645(1-23)Online publication date: 24-Sep-2020
  • Show More Cited By

Index Terms

  1. Combining learn-based and lexicon-based techniques for sentiment detection without using labeled examples

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
      July 2008
      934 pages
      ISBN:9781605581644
      DOI:10.1145/1390334
      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]

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 20 July 2008

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. information retrieval
      2. opinion mining
      3. sentiment classification; opinion mining

      Qualifiers

      • Poster

      Conference

      SIGIR '08
      Sponsor:

      Acceptance Rates

      Overall Acceptance Rate 792 of 3,983 submissions, 20%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)8
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 15 Feb 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2022)A Survey on Sentiment Analysis Techniques for TwitterData Mining Approaches for Big Data and Sentiment Analysis in Social Media10.4018/978-1-7998-8413-2.ch003(57-90)Online publication date: 2022
      • (2021)SSentiA: A Self-supervised Sentiment Analyzer for classification from unlabeled dataMachine Learning with Applications10.1016/j.mlwa.2021.100026(100026)Online publication date: Mar-2021
      • (2020)A mixed approach of statistical weighting method and unsupervised method to improve Uyghur sentiment classificationJournal of Computational Methods in Sciences and Engineering10.3233/JCM-204645(1-23)Online publication date: 24-Sep-2020
      • (2020)¿Tiene carácter predictivo la estructura predicativa [verbo + objeto directo]? Hacia una caracterización sintáctico-semántica para propósitos de análisis de sentimientosLingüística y Literatura10.17533/udea.lyl.n78a0141:78(11-34)Online publication date: 20-Sep-2020
      • (2020)Using data mining to explore the spatial and temporal dynamics of perceptions of metro services in China: The case of ShenzhenEnvironment and Planning B: Urban Analytics and City Science10.1177/239980832097469348:3(449-466)Online publication date: 2-Dec-2020
      • (2020)Social Media Emerging as a Third Eye !! Decoding Users' Sentiment on Government Policy: A Case Study of GST2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4)10.1109/WorldS450073.2020.9210400(116-122)Online publication date: Jul-2020
      • (2020)Detecting Hate Speech in Social Media Articles in Romanized Sinhala2020 20th International Conference on Advances in ICT for Emerging Regions (ICTer)10.1109/ICTer51097.2020.9325465(250-255)Online publication date: 4-Nov-2020
      • (2020)An Unsupervised Sentiment Classification Method Based on Multi-Level Fuzzy Computing and Multi-Criteria FusionIEEE Access10.1109/ACCESS.2020.30148498(145422-145434)Online publication date: 2020
      • (2020)OMLML: a helpful opinion mining method based on lexicon and machine learning in social networksSocial Network Analysis and Mining10.1007/s13278-019-0622-610:1Online publication date: 7-Jan-2020
      • (2019)LeSSA: A Unified Framework based on Lexicons and Semi-Supervised Learning Approaches for Textual Sentiment ClassificationApplied Sciences10.3390/app92455629:24(5562)Online publication date: 17-Dec-2019
      • Show More Cited By

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media