Abstract
Mining emotions over the Internet has seen limited use even though it has important research implications in fields such as applied econometrics, the interdisciplinary study of happiness and well-being, and for various applications in customer relationship management, finance, marketing, human resources, and managerial science.
A key ingredient to making progress in these areas is the development of an emotion specific lexicon, one that can capture intensity and select relevant sentiment laden texts from online sources. An approach to doing this is developed, issues relating to data quality are pointed out, and methods to overcome them are explained.
Justifications for constructing the lexicon are given using state of the art empirical results and research. Then a 10 step algorithm that populates a lexicon using a hybrid procedure (thesaurus-corpus based) is developed. It captures sentiment no matter how it is expressed, and balances issues of speed, cost, and data quality.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Admati, A.R., Pfleiderer, P.: Noisytalk.com: Broadcasting opinions in a noisy environment, WP1970R, Stanford University (2000)
Andreevskaia, A., Bergler, S.: Mining Wordnet for Fuzzy Sentiment: Sentiment Tag Extraction from WordNet Glosses. In: Proceedings of 5th International Conference on Language Resources and Evaluation (LREC) (2006)
Evert, S., Krenn, B.: Methods for the qualitative evaluation of lexical association measures. In: Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics, Toulouse, France (2001)
Fano, R.: Transmission of Information: A Statistical Theory of Communications. American Journal of Physics 29(11) (1961)
Krenn, B.: Empirical Implications on Lexical Association Measures. In: Proceedings of The Ninth EURALEX International Congress, Stuttgart, Germany (2000)
Pazienza, M.T., Pennacchiotti, M., Zanzotto, F.M.: Terminology Extraction: An Analysis of Linguistic & Statistical Approaches. In: Knowledge Mining: Proceedings of the NEMIS 2004 Final Conference (2005)
Sharoff, S.: Creating general-purpose corpora using automated search engine queries. In: WaCky! Working papers on the Web as Corpus (2006)
Sista, S., Srinivasan, S.: Polarized Lexicon for Review Classification. In: Proceedings of the International Conference on Machine Learning, Models, Technologies & Applications (2004)
Sokolova, M., Szpakowicz, S., Nastase, V.: Automatically Building a Lexicon from Raw Noisy Data in a Closed Domain. INTERNEG working papers, INR 01/04 (2004)
Verma, R.: Extraction and Classification of Emotions for Business Research. In: Communications in Computer and Information Science (CCIS), vol. 31, pp. 46–53. Springer, Heidelberg (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Verma, R. (2009). Data Quality Issues and Duel Purpose Lexicon Construction for Mining Emotions. In: Abramowicz, W., Flejter, D. (eds) Business Information Systems Workshops. BIS 2009. Lecture Notes in Business Information Processing, vol 37. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03424-4_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-03424-4_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03423-7
Online ISBN: 978-3-642-03424-4
eBook Packages: Computer ScienceComputer Science (R0)