Evaluation of potential features present in short texts in spanish in order to classify them by polarity

Edgar Casasola; Antonio Leoni de León; Gabriela Marín Raventós

Ayuda

Evaluation of potential features present in short texts in spanish in order to classify them by polarity

Casasola Murillo, Édgar ; Leoni de León, Antonio ^[1] ; Marín Raventós, Gabriela
1. [1] Universidad de Costa Rica
  
  Universidad de Costa Rica
  
  Hospital, Costa Rica
Localización: Káñina: Revista de Artes y Letras de la Universidad de Costa Rica, ISSN 0378-0473, ISSN-e 2215-2636, Vol. 40, Nº. Extra 4, 2016 (Ejemplar dedicado a: Káñina número extraordinario), págs. 21-32
Idioma: español
DOI: 10.15517/rk.v40i4.30223
Enlaces
- Texto completo
Resumen
- This work describes the identification and evaluation process of potential text markers for sentiment analysis. The evaluation of the markers and their use as part of the feature extraction process from plain text that is needed for sentiment analysis is presented. The evaluation of text markers obtained as a result of systematic analysis from a corpus over a second one allowed us to identify that emphasized positive words that tend to appear in positive text posts. The second corpus allowed us to evaluate the relation between the polarity of morphological text markers and the text they appear in. The evaluation of the markers for polarity detection task, in combination with a polarized dictionary, produced polarity classification average precision of 0.56 % using only three markers. These are promising results if we compared them to the top 0.69 % obtained using more features and specialized dictionaries for the same task.
Referencias bibliográficas
- Arce, J. L. 2012. Medios de Comunicación de Masas en Costa Rica: Entre la digita- lización, la convergencia y el auge de los “New Media”....
- Cabanlit, Mark Anthony and Kurt Junshean Espinosa. 2014, July. Optimizing N-gram based text feature selection in sentiment analysis for commercial...
- Cambria, Erick et al. 2013. New avenues in opi- nion mining and sentiment analysis. IEEE Intelligent Systems, 28(2), 15-21. Obtained from...
- Chenlo, J. M. & Losada, D. E. 2014. An empirical study of sentence features for subjectivity and polarity classification. Information...
- Feldman, Ronen. 2013. Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82-89. Obtained from http://dl.acm.org/...
- Forman, G. 2003. An extensive empi- rical study of feature selec- tion metrics for text classification Journal of machine learning research,...
- Guo, Liqiang and Wan, Xiaojun. 2012. Exploiting syntactic and semantic relationships bet- ween terms for opinion retrieval. Journal of the...
- Indurkhya, N. & Damerau, F. J. 2010. Handbook of natural language processing CRC Press, 2.
- Kouloumpis, E.; Wilson, T. & Moore, J. D. 2011. Twitter sentiment analysis: The good the bad and the omg. Icwsm,11, 538-541.
- Martín-Valdivia, María Teresa et al. 2013. Sentiment polarity detection in Spanish reviews combining supervised and unsu- pervised approaches....
- Melero, M.; Cardús, A.-B.; Moreno, A.; Rehm, G.; de Smedt, K. & Uszkoreit, H. (2012). The Spanish language in the digital age. Springer.
- Pang, Bo and Lee, Lillian. 2008. Opinion mining and sentiment analysis. Foundations
- and trends in information retrieval, 2(1- 2), 1-135. Obtained from http://dx.doi. org/10.1561/1500000011
- Perez-Rosas, Verónica et al. 2012, May. Learning Sentiment Lexicons in Spanish. In LREC, 12, 3077-3081.
- Sharma, Anuj and Dey, Shubhamoy. 2012. Performance investigation of feature selection methods and sentiment lexi- cons for sentiment analysis....
- Stats. 2013. Internet World Users By Language: Top 10 Languages. Electronic site. Obtained from http://www.internetworlds- tats.com/stats7.htm
- Turney, Peter D. 2002, July. Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. Proceedings...