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论文范文
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 techniques, semantic orientation and machine learning. With respect to the first technique, approaches use sentiment lexicons to determine the polarity. SentiWordNet is the most used lexicon in the literature [5, 6]. This lexicon is based on WordNet and it contains multiple senses of a word. Also, it provides a positive, objective, and negative value for each sense. Several works using this technique have obtained promising results; however, some other works have not obtained good results due to two main reasons: (1) sentiment lexicons mainly are based on English, which forces researchers to translate the English lexicons to the target language and (2) a word can have different senses depending on the domain where they are used. Regarding the machine learning approach, authors use classification algorithms such as Support Vector Machines (SVM) [7–11], Bayesian Networks (BayesNet) [12], and decision trees (J48) [10], among others. For this technique, two data sets are necessary, a training set and an evaluation set. The training set is used for the algorithm to learn from features of the domain. Meanwhile, the evaluation set is used to validate the built model from the training set. The performance of the machine learning technique depends on the effectiveness of the selected method for feature extraction. Among the most used methods are bag of words [13], TF-IDF [14], -grams (unigrams, bigrams, and trigrams) [11, 15], features based on POS tagging [16], and features based on dependency rules [17]. 
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