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Open AccessArticle

Sentiment Analysis Based on Deep Learning: A Comparative Study

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Department of Information Technology, HoChiMinh City University of Transport (UT-HCMC), Ho Chi Minh 70000, Vietnam
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Data Mining (MIDA) Research Group, University of Salamanca, 37007 Salamanca, Spain
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Biotechnology, Intelligent Systems and Educational Technology (BISITE) Research Group, University of Salamanca, 37007 Salamanca, Spain
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Author to whom correspondence should be addressed.
Electronics 2020, 9(3), 483; https://doi.org/10.3390/electronics9030483
Received: 31 January 2020 / Revised: 9 March 2020 / Accepted: 10 March 2020 / Published: 14 March 2020
(This article belongs to the Section Artificial Intelligence)
The study of public opinion can provide us with valuable information. The analysis of sentiment on social networks, such as Twitter or Facebook, has become a powerful means of learning about the users’ opinions and has a wide range of applications. However, the efficiency and accuracy of sentiment analysis is being hindered by the challenges encountered in natural language processing (NLP). In recent years, it has been demonstrated that deep learning models are a promising solution to the challenges of NLP. This paper reviews the latest studies that have employed deep learning to solve sentiment analysis problems, such as sentiment polarity. Models using term frequency-inverse document frequency (TF-IDF) and word embedding have been applied to a series of datasets. Finally, a comparative study has been conducted on the experimental results obtained for the different models and input features. View Full-Text
Keywords: sentiment analysis; deep learning; machine learning; neural network; natural language processing sentiment analysis; deep learning; machine learning; neural network; natural language processing
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Dang, N.C.; Moreno-García, M.N.; De la Prieta, F. Sentiment Analysis Based on Deep Learning: A Comparative Study. Electronics 2020, 9, 483.

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