Abstract
Today, millions of message posted daily contain opinions of users in a variety of languages, including emoticon. Sentiment analysis becomes a very difficult task, and the understanding and knowledge of the problem and its solution are still preliminary. Therefore, this work presents a new methodology, called Concept-based Sentiment Analysis (C-SA). The main mechanism of the C-SA is Msent-WordNet (Multilingual Sentiment WordNet), which is used to prove and increase the results accuracy of sentiment analysis. By using the Msent-WordNet, all words in opinion texts having similar sense or meaning will be denoted and considered as a same concept. Indeed, concept-level sentiment analysis aims to go beyond a mere word-level analysis of text and provide novel approaches to sentiment analysis that enables a more efficient solution from opinion text. This can help to reduce the inherent ambiguity and contextual nature of human languages. Finally, the proposed methodology is validated through sentiment classification.
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References
Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity: an exploration of features for phrase-level sentiment analysis. Assoc. Comput. Linguist. 35 (2009)
Liu, B.: Sentiment Analysis and Subjectivity. In: Handbook of Natural Language Processing. CRC Press, Taylor and Francis Group (2010)
Smith, P.: Sentiment analysis: beyond polarity. Thesis Proposal, School of Computer Science, University of Birmingham, UK (2011)
Reddy, A.S.S.: Polarity analysis through neutralization of non-polar words and segregation of polar words using training data. Int. J. Comput. Sci. Inf. Technol. 3(5), 5176–5178 (2012)
Saif, H., Fernandez, M., He, Y., Alani, H.: SentiCircles for contextual and conceptual semantic sentiment analysis of Twitter. In: The Semantic Web: Trends and Challenges, pp. 83–98 (2014)
Funk, A., Li, Y., Saggion, H., Bontcheva, K., Leibold, C.: Opinion analysis for business intelligence applications. In: First international workshop on Ontology-Supported Business Intelligence (at ISWC). ACM, Karlsruhe (2008)
Plaza, L., de Albornoz, J.C.: Sentiment analysis in business intelligence: a survey. In: Customer Relationship Management and the Social and Semantic Web: Enabling Cliens Conexus, pp. 231–252 (2012)
Tsai, F.T., Lu, H.M., Hung, M.W.: The effects of news sentiment and coverage on credit rating analysis. In: The Pacific Asia Conference on Information Systems (PACIS) (2010)
Colleoni, E., Arvidsson, A., Hansen, L.K.. Marchesini, A.: monitoring corporate reputation in social media using real-time sentiment analysis. In: 15th International Conference on Corporate Reputation: Navigating the Reputation Economy, New Orleans, USA (2011)
Polpinij, J.: Multilingual sentiment classification on large textual data, In: IEEE 4th International Conference on Big Data and Cloud Computing (2014)
Denecke, K.: Using SentiWordNet for multilingual sentiment analysis, In: IEEE 24th International Conference on Data Engineering Workshop (ICDEW), pp. 507−512 (2008)
Balahur, A., Turchi, M.: Comparative experiments for multilingual sentiment analysis using machine translation. In: Proceedings of the First International Workshop on Sentiment Discovery from Affective Data (SDAD) (2012)
Balahur, A., Turchi, M.: Multilingual sentiment analysis using machine translation? In: Proceedings of the 3rd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, pp. 52−60 (2012)
Banea, C., Mihalcea, R., Wiebe, J.: A bootstrapping method for building subjectivitylexicons for languages with scarce resources. In: Proceedings of the Conference on Language Resources and Evaluations (LREC) (2008)
Banea, C., Mihalcea, R., Wiebe, J.: Multilingual subjectivity: are more languages better? In: Proceedings of the International Conference on Computational Linguistics (COLING), pp. 28−36 (2010)
Kim, J., Li, J.J., Lee, J.H.: Evaluating multilanguage-comparability of subjectivity analysis systems. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (2010)
Steinberger, J., Lenkova, P., Ebrahim, M., Ehrman, M., Hurriyetoglu, A., Kabadjov, M., Steinberger, R., Tanev, H., Zavarella, V., Vazquez, S.: Creating sentiment dictionaries via triangulation. In: Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, Portland, Oregon (2011)
Tromp, E., Pechenizkiy, M.: SentiCorr: Multilingual sentiment analysis of personal correspondence. In: IEEE 11th International Conference on Data Mining Workshops (ICDMW) (2011)
Hu, X., Tang, J., Gao, H., Liu, H.: Unsupervised sentiment analysis with emotional signals. In: Proceedings of the 22nd International Conference on World Wide Web (2013)
Miller, G.A.: WordNet: a lexical database for English. Mag. Commun. ACM 38(11), 39–41 (1995)
Mekpiroon, O., Tammarattananont, P., Apitiwongmanit, N., Buasroung, N., Pravalpruk, B., Supnithi, T.: Dictionary-based translation feature in open source LMS: a case study of Thai LMS: LearnSquare. In: The 8th IEEE International Conference on Advanced Learning Technologies (2008)
Coden, A., Gruhl, D., Lewis, N., Mendes, P. N., Nagarajan, M., Ramakrishnan, C., Welch, S.: Semantic lexicon expansion for concept-based aspect-aware sentiment analysis. In: Semantic Web Evaluation Challenge Communications in Computer and Information Science, pp. 34−40 (2014)
Meknavin, S., Charoenpornsawat, P., Kijsirikul, B.: Feature-based thai word segmentation. In: Proceedings of the Natural Language Processing Pacific Rim Symposium (NLPRS) (1997)
Baeza-Yates, R.A., Ribeiro-Neto, B.: Modern Information Retrieval. ACM Press, Addison-Wesley, New York (1999)
Nigam, K., McCallum, A., Thrun, S., Mitchell, T.: Text classification from labeled and unlabeled document using EM. Mach. Learn. 39(2/3), 103–134 (2000)
Wu, X., Kumar, V., Quinlan, R.J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, A., Ng, G.J., Liu, B., Yu, P.S., Zhou, Z.H., Steinbach, M., Hand, D.J., Steinberg, D.: Top 10 algorithms in data mining. Knowl. Inf. Syst. 14, 1–37 (2008)
Aggarwal, C.C., Zhai, C.X.: A survey of text classification algorithms. In: Mining Text Data, pp. 163−222 (2012)
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Polpinij, J., Srikanjanapert, N., Wongsin, C. (2016). Concept-Based Sentiment Analysis for Opinion Texts with Multiple-Languages. In: Meesad, P., Boonkrong, S., Unger, H. (eds) Recent Advances in Information and Communication Technology 2016. Advances in Intelligent Systems and Computing, vol 463. Springer, Cham. https://doi.org/10.1007/978-3-319-40415-8_4
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DOI: https://doi.org/10.1007/978-3-319-40415-8_4
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