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

Sentiment Analysis in Spanish for Improvement of Products and Services: A Deep Learning Approach Mario Andrés Paredes-Valverde,1 Ricardo Colomo-Palacios,2 María del Pilar Salas-Zárate,1 and Rafael Valencia-García1 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. Abstract 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. 1. Introduction Nowadays, there is a lot of online opinions. This information is important for users because it helps them to make decisions about buying a product, voting in a political election, and choosing a travel destination, among other subjects. This information is also important for organizations since it helps them to know the general opinion about their products, the sales forecast, and the customer satisfaction in real time. Based on this information, companies can identify opportunities for improving the quality of their products or services. A good example that demonstrates the importance of the opinions is a t-shirt of Zara clothing store which received negative opinions because it looked like the clothes used in the Holocaust. In these situations, companies must act quickly and solve the problem to avoid these opinions affecting their reputation. In this sense, to know the public opinion in real time is very important. Twitter is a social network, where users share information on almost everything in real time. Therefore, companies consider this social network as a rich source of information that allows knowing the general opinion about their products and services, among others [1]. However, analyzing and processing all these opinions require much time and effort for the humans. On these grounds, a technology that processes automatically this information has arisen. This technology is known as sentiment analysis or opinion mining. Sentiment analysis has been defined by several authors. However the definition most used in the research community is the proposed by Liu [2], who defined it as follows: “Sentiment analysis is the field of study that analyzes people’s opinions, sentiments, evaluations, appraisals, attitudes, and emotions towards entities such as products, services, organizations, individuals, issues, events, topics, and their attributes.” In the last years, several approaches have been proposed for sentiment analysis. Most of these approaches are based on two main techniques, semantic orientation and machine learning. Although good results were obtained for both techniques, several works in the literature have demonstrated that machine learning obtained better results. However, in more recent years a new technique known as deep learning has captured the attention of researchers because it has significantly outperformed traditional methods [3, 4]. Most of the deep-learning-based approaches for sentiment analysis are based on the English language. Hence, we propose a deep-learning-based approach for sentiment analysis of tweets in Spanish. Spanish is the third language most used on the Internet (http://www.internetworldstats.com/stats7.htm). Therefore, we consider that new approaches for sentiment analysis in the Spanish language are necessary. The remainder of the paper is structured as follows. Section 2 presents a review of the literature about sentiment analysis and deep learning. Section 3 described the proposed approach. The experiments and results are presented in Section 4. Finally, Section 5 presents conclusions and future work. 2. Related Works In the literature, several authors have proposed approaches for the sentiment analysis. These works have used two main t (...truncated)