Authors:
Luis G. Moreno-Sandoval
1
;
Paola Beltrán-Herrera
1
;
Jaime A. Vargas-Cruz
1
;
Carolina Sánchez-Barriga
1
;
Alexandra Pomares-Quimbaya
2
;
Jorge A. Alvarado-Valencia
2
and
Juan C. García-Díaz
1
Affiliations:
1
Colombian Center of Excellence and Appropriation on Big Data and Data Analytics (CAOBA), Colombia
;
2
Pontificia Universidad Javeriana, Colombia
Keyword(s):
Spanish Lexicon, Spanish Resources for Sentiment Analysis, Polarity’s Classification, Opinion Mining, Sentiment Analysis.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
Opinion mining and sentiment analysis in texts from social networks such as Twitter has taken great
importance during the last decade. Quality lexicons for the sentiment analysis task are easily found in
languages such as English; however, this is not the case in Spanish. For this reason, we propose CSL, a
Combined Spanish Lexicon approach for sentiment analysis that uses an ensemble of six lexicons in Spanish
and a weighted bag of words strategy. In order to build CSL we used 68,019 tweets previously classified by
researchers at the Spanish Society of Natural Language Processing (SEPLN) obtaining a precision of 62.05
and a recall of 60.75 in the validation set, showing improvements in both measurements. Additionally, we
compare the results of CSL with a very well-known commercial software for sentiment analysis in Spanish
finding an improvement of 10 points in precision and 15 points in recall.