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Scientific Programming
Volume 2017 (2017), Article ID 1329281, 6 pages
Research Article

Sentiment Analysis in Spanish for Improvement of Products and Services: A Deep Learning Approach

1Departamento de Informática y Sistemas, Universidad de Murcia, 30100 Murcia, Spain
2Computer Science Department, Østfold University College, Holden, Norway

Correspondence should be addressed to María del Pilar Salas-Zárate

Received 16 June 2017; Accepted 27 August 2017; Published 26 October 2017

Academic Editor: Jezreel Mejia-Miranda

Copyright © 2017 Mario Andrés Paredes-Valverde et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Sentiment analysis is an important area that allows knowing public opinion of the users about several aspects. This information helps organizations to know customer satisfaction. Social networks such as Twitter are important information channels because information in real time can be obtained and processed from them. In this sense, we propose a deep-learning-based approach that allows companies and organizations to detect opportunities for improving the quality of their products or services through sentiment analysis. This approach is based on convolutional neural network (CNN) and word2vec. To determine the effectiveness of this approach for classifying tweets, we conducted experiments with different sizes of a Twitter corpus composed of 100000 tweets. We obtained encouraging results with a precision of 88.7%, a recall of 88.7%, and an -measure of 88.7% considering the complete dataset.