Exploring Convolutional Neural Networks for Sentiment Analysis of Spanish tweets

Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 1014–1022, Valencia, Spain, April 3-7, 2017. c⃝2017 Association for Computational Linguistics Exploring Convolutional Neural Networks for Sentiment Analysis of Spanish tweets Isabel Segura-Bedmar1, Antonio Quir´os2 and Paloma Mart´ınez1 1Computer Science Department, Universidad Calos III de Madrid, Madrid, Spain 2s|ngular, Data & Analytics division, Madrid, Spain {isegura,pmf}@inf.uc3m.es {antonio.quiros}@sngular.team Abstract Spanish is the third-most used language on the Internet, after English and Chinese, with a total of 7.7% of Internet users (more than 277 million of users) and a huge users growth of more than 1,400%. However, most work on sentiment analysis has fo- cused on English. This paper describes a deep learning system for Spanish senti- ment analysis. To the best of our knowl- edge, this is the first work that explores the use of a convolutional neural network to polarity classification of Spanish tweets. 1 Introduction Knowing the opinion of customers or users has be- come a priority for companies and organizations in order to improve the quality of their services and products. With the ongoing explosion of social media, it affords a significant opportunity to poll the opinion of many internet users by processing their comments. However, it should be noted that sentiment analysis, which can be defined as the au- tomatic analysis of opinion in texts (Pang and Lee, 2008), is a challenging task because even different people often assign different polarities to a given text. Moreover, sentiment analysis can involve several Natural Language Processing (NLP) tasks such as negation or subjectivity detection, which have not been fully resolved by the NLP research community to date. On Twitter, the task is even more difficult, because the texts are small (only 140 characters) and are characterized by a infor- mal style language utilized by users, many gram- matical errors and spelling mistakes, slang and vulgar vocabulary, and plenty of abbreviations. The shortage of training and testing data is one of the main bottlenecks for most NLP tasks in gen- eral, and for sentiment analysis in particular. This drawback has been partially overcome thanks to the organization of shared tasks such as Sentiment Analysis in Twitter Task at SemEval 2013-2015 (Nakov et al., 2013; Rosenthal et al., 2014; Rosen- thal et al., 2015; Nakov et al., 2016), which pro- vided annotated corpora of tweets for sentiment analysis. Most research efforts in this task have focused on English texts, much less attention has been given to other languages (Abdul-Mageed et al., 2011; Kapukaranov and Nakov, 2015). How- ever, Spanish is the third language most used on the internet, with a total of 7.7% (more than 277 million of users) and a huge internet growth of more than 1,400%. Since its introduction in 2013, the workshop on Sentiment Analysis at SEPLN (Villena-Rom´an et al., 2013; Villena-Rom´an et al., 2015b; Villena- Rom´an et al., 2015a; Garc´ıa Cumbreras et al., 2016) has had as main goal to promote the de- velopment of methods and …

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