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

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.

Sentiment Analysis Based on Deep Learning: A Comparative Study

“…The authors described how they used deep learning models to increase the accuracy of their respective sentiment analysis. Most of the models are used for content written in English, but there are a few that manage tweets in other languages, including Spanish [51], Thai [28], and Persian [47]. Previous researchers have analyzed tweets by applying different models of polarity-based sentiment deep learning.…”

Document Level Sentiment Analysis: A survey

“…In the last years, deep learning approaches have captured the attention of researchers because it has significantly outperformed traditional methods. For example, in [38] Paredes-Valverde et al, proposed a deep learning approach to build a classifier for sentiment detection. This approach is divided into three main modules: (1) pre-processing module, (2) word embeddings, and (3) CNN model.…”

“…Table 1 grouped the surveyed works into these categories. [6], [10], [13], [14], [17], [19], [23], [26], [28], [30], [5] Lexicon based [2], [18], [20], [24] Hybrid [3], [12], [29] Deep learning approaches [38] [39] From Table 1, the supervised approach is the dominant one compared to the other approaches. divided into dictionary-based and corpus-based.…”

“…We have collected the various techniques used, Table 2 presents the results. RNA [3] word embeddings& CNN [38] LSTM-GRNN [39] From Table 2, the most used methods seem to be based on SVM and NB, for the supervised approach. For unsupervised approach, dictionary-based approaches are the most frequent across the majority of works.…”

Personalized Recommendations Based on Sentimental Interest Community Detection

“…Sentiment analysis, or opinion mining, has been applied to many fields, such as marketing, opinion monitoring, and information retrieval [7]. Sentiment analysis includes positive, negative, or neutral attitudes.…”