Abstract
Sentiment analysis methods aim at identifying the polarity of a piece of text, e.g., passage, review, snippet, by analyzing lexical features at the level of the terms or the sentences. However, many of the previous works do not utilize features that can offer a deeper understanding of the text, e.g., negation phrases. In this work we demonstrate a novel piece of software, namely PYTHIA1, which combines semantic and lexical features at the term and sentence level and integrates them into machine learning models in order to predict the polarity of the input text. Experimental evaluation of PYTHIA in a benchmark movie reviews dataset shows that the suggested combination performs favorably against previous related methods. An online demo is publicly available at http://omiotis.hua.gr/pythia .
Chapter PDF
Similar content being viewed by others
References
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C.D., Ng, A.Y., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank. In: EMNLP, pp. 1631–1642. ACL (2013)
Sinha, R., Mihalcea, R.: Unsupervised graph-based word sense disambiguation using measures of word semantic similarity. In: Proc. of the IEEE ICSC, pp. 363–369 (2007)
Panagiotopoulou, V., Varlamis, I., Androutsopoulos, I., Tsatsaronis, G.: Word sense disambiguation as an integer linear programming problem. In: Maglogiannis, I., Plagianakos, V., Vlahavas, I. (eds.) SETN 2012. LNCS, vol. 7297, pp. 33–40. Springer, Heidelberg (2012)
Tsatsaronis, G., Varlamis, I., Vazirgiannis, M.: Text relatedness based on a word thesaurus. JAIR 37, 1–39 (2010)
Nguyen, K., Ock, C.: Word sense disambiguation as a travelling salesman problem. AI Review, 1–23 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Katakis, I.M., Varlamis, I., Tsatsaronis, G. (2014). PYTHIA: Employing Lexical and Semantic Features for Sentiment Analysis. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2014. Lecture Notes in Computer Science(), vol 8726. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44845-8_32
Download citation
DOI: https://doi.org/10.1007/978-3-662-44845-8_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-44844-1
Online ISBN: 978-3-662-44845-8
eBook Packages: Computer ScienceComputer Science (R0)