Advertisement

Soft Computing

, Volume 20, Issue 9, pp 3411–3420 | Cite as

Hierarchical classification in text mining for sentiment analysis of online news

  • Jinyan LiEmail author
  • Simon Fong
  • Yan Zhuang
  • Richard Khoury
Focus

Abstract

Sentiment analysis in text mining is a challenging task. Sentiment is subtly reflected by the tone and affective content of a writer’s words. Conventional text mining techniques, which are based on keyword frequencies, usually run short of accurately detecting such subjective information implied in the text. In this paper, we evaluate several popular classification algorithms, along with three filtering schemes. The filtering schemes progressively shrink the original dataset with respect to the contextual polarity and frequent terms of a document. We call this approach “hierarchical classification”. The effects of the approach in different combination of classification algorithms and filtering schemes are discussed over three sets of controversial online news articles where binary and multi-class classifications are applied. Meanwhile we use two methods to test this hierarchical classification model, and also have a comparison of the two methods.

Keywords

Sentiment analysis Text mining  Classification 

Notes

Acknowledgments

The authors are thankful for the financial support from the research Grants of Grant No. MYRG152 (Y3-L2)-FST11-ZY, and FDCT 019/2011/A1, offered by the University of Macau and Macau SAR government.

References

  1. Agrawal R, Rajagopalan S, Srikant R, Xu Y (2003) Mining newsgroups using networks arising from social behavior. In: Proceedings of the 12th international conference on World Wide Web. ACM, pp 529–535Google Scholar
  2. Argamon S, Bloom K, Esuli A, Sebastiani F (2009) Automatically determining attitude type and force for sentiment analysis. Human Language Technology. Challenges of the Information Society. Springer, Berlin, Heidelberg, pp 218–231Google Scholar
  3. Cerini S, Compagnoni V, Demontis A, Formentelli M, Gandini G (2007) Language resources and linguistic theory: typology, second language acquisition, English linguistics (Forthcoming), chapter Micro-WNOp: A gold standard for the evaluation of automatically compiled lexical resources for opinion mining. Franco Angeli Editore, MilanGoogle Scholar
  4. Chaovalit P, Zhou L (2005) Movie review mining: a comparison between supervised and unsupervised classification approaches. In: System Sciences, 2005. HICSS’05. Proceedings of the 38th Annual Hawaii International Conference on. IEEEGoogle Scholar
  5. Dave K, Lawrence S, Pennock DM (2003) Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In: Proceedings of the 12th international conference on World Wide Web. ACM, pp 519–528Google Scholar
  6. Devitt A, Ahmad K (2007) Sentiment polarity identification in financial news: a cohesion-based approachGoogle Scholar
  7. Esuli A, Sebastiani F (2005) Determining the semantic orientation of terms through gloss classification. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management. ACM, pp 617–624Google Scholar
  8. Fong S, Zhuang Y, Li J, Khoury R (2013) (2013) Sentiment analysis of online news using MALLET. In: Computational and Business Intelligence (ISCBI), 2013 International Symposium on. IEEE, pp 301–304Google Scholar
  9. Forman G (2003) An extensive empirical study of feature selection metrics for text classification. J Mach Learn Res 3:1289–1305zbMATHGoogle Scholar
  10. Hatzivassiloglou V, McKeown KR (1997) Predicting the semantic orientation of adjectives. In: Proceedings of the 35th annual meeting of the association for computational linguistics and eighth conference of the european chapter of the association for computational linguistics. Association for Computational Linguistics, pp 174–181Google Scholar
  11. Hernández L, López-Lopez A, Medina JE (2009) Recognizing polarity and attitude of words in text. In: New trends in artificial intelligence, Procs. 14th Portuguese Conference on Artificial Intelligence. EPIA, pp 12–15Google Scholar
  12. Kamps J, Marx M, Mokken RJ, De Rijke M (2004) Using WordNet to measure semantic orientations of adjectives. LREC 4:1115–1118Google Scholar
  13. Kim SM, Hovy E (2004) Determining the sentiment of opinions. In: Proceedings of the 20th international conference on Computational Linguistics. Association for Computational LinguisticsGoogle Scholar
  14. Kim SM, Hovy EH (2007) Crystal: analyzing predictive opinions on the Web. In: EMNLP-CoNLL. pp 1056–1064Google Scholar
  15. Pang B, Lee L (2005) Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics, pp 115–124Google Scholar
  16. Pang B, Lee L, Vaithyanathan S (2002) Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP). pp 79–86 Google Scholar
  17. Rajaraman A, Ullman JD (2012) Mining of massive datasets, vol 77. Cambridge University Press, CambridgeGoogle Scholar
  18. Snyder B, Barzilay R (2007) Multiple aspect ranking using the good grief algorithm. In: Proceedings of the Joint Human Language Technology/North American Chapter of the ACL Conference (HLT-NAACL). pp 300–307Google Scholar
  19. Takamura H, Inui T, Okumura M (2005) Extracting semantic orientations of words using spin model. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics, pp. 133–140Google Scholar
  20. Turney P (2002) Thumbs up or thumbs down’s semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the Association for Computational Linguistics. pp. 417–424. arXiv:cs.LG/0212032
  21. Turney PD, Littman M (2003) Measuring praise and criticism: Inference of semantic orientation from association. ACM Trans Inf Syst 21(4):315–346CrossRefGoogle Scholar
  22. Whitelaw C, Garg N, Argamon S (2005) Using appraisal groups for sentiment analysis. In: Proceedings of the 14th ACM international conference on Information and knowledge management. ACM, pp 625–631Google Scholar
  23. Wiebe J (1994) Tracking point of view in narrative. Computational Linguistics, 20. R. Nicole, Title of paper with only first word capitalized. J Name Stand Abbrev (in press)Google Scholar
  24. Wilson TA (2008) Fine-grained subjectivity and sentiment analysis: recognizing the intensity, polarity, and attitudes of private states. ProQuestGoogle Scholar
  25. Yang Y, Pedersen JO (1997) A comparative study on feature selection in text categorization. ICML 97:412–420Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Jinyan Li
    • 1
    Email author
  • Simon Fong
    • 1
  • Yan Zhuang
    • 1
  • Richard Khoury
    • 2
  1. 1.Department of Computer and Information ScienceUniversity of MacauTaipaMacau SAR
  2. 2.Department of Software EngineeringLakehead UniversityThunder BayCanada

Personalised recommendations