PARAMETRICAL WORDS IN THE SENTIMENT LEXICON

Authors

  • Elena G. Brunova Head of the Department of Foreign Languages and Cross-Cultural Communication, Tyumen State University

Keywords:

cognitive linguistics, natural language processing, sentiment analysis, lexicon, domain, parametrical words, increment, decrement

Abstract

In this paper, the main features of parametrical words within a sentiment lexicon are determined. The data for the research are client reviews in the Russian language taken from the bank client rating; the domain under study is bank service quality. The sentiment lexicon structure is presented; it includes two primary classes (positive and negative words) and three secondary classes (increments, polarity modifiers, and polarity anti-modifiers). This lexicon is used as the main tool for the sentiment analysis carried out by two methods: the Naïve Bayes classifier and the REGEX algorithm. 
Parametrical words are referred to as the words denoting the value of some domain-specific parameter, e.g. the client’s time consuming. To distinguish the main features of parametrical words, the parameters relevant for the bank service quality domain are determined. The revised lexicon structure is proposed, with a new class (decrements) added. The results of the research demonstrate that parametrical words express implicit opinions, since parameters are not usually named directly in reviews. Only a small number of parametrical words can be ranged into the primary classes (positive or negative), but this ranging is domain-specific. It is the parameter that determines the domain specificity of such words. Most parametrical words are ranged into the secondary classes, and this ranging can be considered universal. The parametrical words denoting the increase of a parameter should be ranged into the increment class, as they intensify positive or negative emotions. The parametrical words denoting the decrease of a parameter should be ranged into the decrement class, as they reduce positive or negative emotions. The evident progress on the way to the sentiment lexicon universalization can be achieved by classifying parametrical words within the sentiment lexicon.

Downloads

Download data is not yet available.

References

Brunova, E.G. (2012). Metodika Sostavleniya Otsenochnogo Leksikona dlya Kontent-Analiza Mneniy (Technique of Constructing a Sentiment Lexicon) Language and Science. No. 1. (Online). Available: http://www.utmn.ru/docs/9317.pdf (in Russian)

Gamon M., et al. (2005). Pulse: Mining Customer Opinions from Free Text. Proc. of the 6th International Symposium on Intelligent Data Analysis (IDA). P. 121-132.

Ganapathibhotla, M., Liu B. (2008). Mining Opinions in Comparative Sentences. Proc. of the 22nd International Conference on Computational Linguistic. Manchester. P. 241–248.

Hatzivassiloglou V., McKeown K. (1997). Predicting the Semantic Orientation of Adjectives. Proc. of the 35th AnnualMeeting of ACL, Madrid. P. 174-181.

Hu M., Liu B. (2004). Mining and summarizing customer reviews. Proc. of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. P. 168-177.

Liu, B.(2010). Sentiment Analysis and Subjectivity. Handbook of Natural Language Processing, Second Edition. (Online). Available: http://www.cs.uic.edu/~liub/FBS/NLP-handbook-sentiment-analysis.pdf

Lukashevich, N.B., Chetverkin I.I. (2011). Izvlecheniye i Ispolsovaniye otsenochnykh Slov v Zadache Klassifikatsii Otzyvov na Tri Klassa (Extracting and Appliction of Sentiment Words in the Task of Three-Class Review Classification). Vychislitelnye Metody i Programmirovaniye. Vol. 12. P. 73-81. (in Russian).

Manning С., Raghavan P, Schütze H. (2008). Introduction to Information Retrieval. Cambridge: Cambridge University Press. 544 p.

Nasukawa T., Yi J. (2003). Sentiment Analysis: Capturing Favorability Using Natural Language Processing. Proc. of the 2nd International Conference on Knowledge Capture. Florida. P. 70-77.

Pang B., Lee L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval. Vol. 2, No 1-2. P. 1–135.

Pang B., Lee L., Vaithyanathan S. (2002). Thumbs up? Sentiment Classification using Machine Learning Techniques. Proc. of EMNLP. (Online). Available:http://www.cs.cornell.edu/home/llee/papers/sentiment.pdf

Turney P. (2002). Thumbs Up or Thumbs Down?: Semantic Orientation Applied to Unsupervised Classification of Reviews. Proc. of the 40th Annual Meeting on Association for Computational Linguistics. P. 417-424.

Webb, G. et al. (2005). Not So Naive Bayes: Aggregating One-Dependence Estimators. Machine Learning. 58. P. 5-24.

Wiebe J., Wilson T., Bell M. (2001). Identifying Collocations for Recognizing Opinions. Proc. of ACL/EACL 01 Workshop on Collocation.

www.banki.ru

Downloads

Published

2013-12-20

How to Cite

G. Brunova, E. (2013). PARAMETRICAL WORDS IN THE SENTIMENT LEXICON. International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 1(2), 57–64. Retrieved from https://ijcrsee.com/index.php/ijcrsee/article/view/7