Web Service SWePT: A Hybrid Opinion Mining Approach

Yolanda Raquel Baca-Gomez (Center for Research and Innovation in Information and Communications Technologies, Mexico)

Alicia Martinez (National Center for Research and Technology Developmen, Mexico)

Paolo Rosso (Technical University of Valencia, Spain)

Hugo Estrada (Center for Research and Innovation in Information and Communications Technologies, Mexico)

Delia Irazu Hernandez Farias (Technical University of Valencia, Spain)

Abstract: The increasing use of social networks and online sites where people can express their opinions has created a growing interest in Opinion Mining. One of the main tasks of Opinion Mining is to determine whether an opinion is positive or negative. Therefore, the role of the feelings expressed on the web has become crucial, mainly due to the concern of businesses and government to automatically identify the semantic orientation of the views of customers or citizens. This is also a concern, in the area of health to identify psychological disorders. This research focuses on the development of a web application called SWePT (Web Service for Polarity detection in Spanish Texts), which implements the Sequential Minimal Optimization (SMO) algorithm, extracting its features from an affective lexicon in Mexican Spanish. For this purpose, a corpus and an affective lexicon in Mexican Spanish were created. The experiments using three (positive, neutral, negative) and five categories (very positive, positive, neutral, negative, and very negative) allow us to demonstrate the effectiveness of the presented method. SWePT has also been implemented in the Emotion-bracelet interface, which shows the opinion of a user graphically.

Keywords: affective lexicon, hybrid approach, opinion mining, web service

Categories: C.3, H.3.1, H.3.5, H.5.2, I.2.6, I.2.7, J.4