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Soft Computing

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04.08.2015 | Focus

Hierarchical classification in text mining for sentiment analysis of online news

verfasst von: Jinyan Li, Simon Fong, Yan Zhuang, Richard Khoury

Erschienen in: Soft Computing | Ausgabe 9/2016

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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.

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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 529–535

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 218–231

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, Milan

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. IEEE

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 519–528

Devitt A, Ahmad K (2007) Sentiment polarity identification in financial news: a cohesion-based approach

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 617–624

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 301–304

Forman G (2003) An extensive empirical study of feature selection metrics for text classification. J Mach Learn Res 3:1289–1305MATH

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 174–181

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 12–15

Kamps J, Marx M, Mokken RJ, De Rijke M (2004) Using WordNet to measure semantic orientations of adjectives. LREC 4:1115–1118

Kim SM, Hovy E (2004) Determining the sentiment of opinions. In: Proceedings of the 20th international conference on Computational Linguistics. Association for Computational Linguistics

Kim SM, Hovy EH (2007) Crystal: analyzing predictive opinions on the Web. In: EMNLP-CoNLL. pp 1056–1064

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 115–124

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

Rajaraman A, Ullman JD (2012) Mining of massive datasets, vol 77. Cambridge University Press, Cambridge

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 300–307

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. 133–140

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

Turney PD, Littman M (2003) Measuring praise and criticism: Inference of semantic orientation from association. ACM Trans Inf Syst 21(4):315–346CrossRef

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 625–631

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)

Wilson TA (2008) Fine-grained subjectivity and sentiment analysis: recognizing the intensity, polarity, and attitudes of private states. ProQuest

Yang Y, Pedersen JO (1997) A comparative study on feature selection in text categorization. ICML 97:412–420

Titel: Hierarchical classification in text mining for sentiment analysis of online news
verfasst von: Jinyan Li
Simon Fong
Yan Zhuang
Richard Khoury
Publikationsdatum: 04.08.2015
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 9/2016
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-015-1812-4

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