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Sentiment Analysis Based on Deep Learning: A Comparative Study

Open access
Dang, Nhan Cach, Moreno-García, María N., De la Prieta, Fernando·2020·Electronics

Abstract

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.

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Metadata

Authors
Dang, N. C., Moreno-García, M. N., & De la Prieta, F.
Venue
Electronics
Publisher
MDPI AG
Published
2020-03-14
Volume
9
Issue
3
Pages
483
ISSN
2079-9292 (ISSN-L)
OpenAlex
W3011570378
Cited by
593
References
51
FWCI
27.84
Type
journal article
Language
en
Open access
gold

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