Abstract
Opinions expressed by text documents freely written in various natural languages represent a valuable source of knowledge that is hidden in large datasets. The presented research describes a text mining-method how to discover words that are significant for expressing different opinions (positive and negative). The method applies a simple but unified data pre-processing for all languages, providing the bag-of-words with words represented by their frequencies in the data. Then, the frequencies are used by the algorithm which generates decision trees. The tree decisive nodes contain the words that are significant for expressing the opinions. Positions of these words in the tree represent their significance degree, where the most significant word is in the node. As a result, a list of relevant words can be used for creating a dictionary containing only relevant information. The described method was tested using very large sets of customers’ reviews concerning the on-line hotel room booking. For more than 15 languages, there were available several millions of reviews. The resulting dictionaries included only about 200 significant words.
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References
Berry, M.W., Kogan, J. (eds.): Text Mining: Applications and Theory. John Wiley & Sons, Chichester (2010)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2007)
c5/See5 (2011), http://www.rulequest.com/see5-info.html
Dařena, F., Žižka, J.: Text Mining-Based Formation of Dictionaries Expressing Opinions in Natural Languages. In: Proceedings of the 17th International Conference on Soft Computing Mendel 2011, Brno, June 15-17, pp. 374–381 (2011) ISSN: 1803-3814
Liu, B.: Web data mining: Exploring Hyperlinks, Contents, and Usage Data. In: Opinion Mining. Springer, Heidelberg (2006)
Nie, J.Y.: Cross-Language Information Retrieval. Synthesis Lectures on Human Language Technologies 3(1), 1–125 (2010)
Peng, F., Huang, X.: Machine learning for Asian language text classiffication. Journal of Documentation 63(3), 378–397 (2007)
Sebastiani, F.: Machine Learning in Automated Text Categorization. ACM Computing Surveys 1, 1–47 (2002)
Shmueli, G., Patel, N.R., Bruce, P.C.: Data Mining for Business Intelligence. John Wiley & Sons, Chichester (2010)
Žižka, J., Dařena, F.: Automatic Sentiment Analysis Using the Textual Pattern Content Similarity in Natural Language. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds.) TSD 2010. LNCS, vol. 6231, pp. 224–231. Springer, Heidelberg (2010)
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Žižka, J., Dařena, F. (2011). Mining Significant Words from Customer Opinions Written in Different Natural Languages. In: Habernal, I., Matoušek, V. (eds) Text, Speech and Dialogue. TSD 2011. Lecture Notes in Computer Science(), vol 6836. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23538-2_27
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DOI: https://doi.org/10.1007/978-3-642-23538-2_27
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