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Sentiment Analysis Using Deep Learning Approach

Peng Cen1, Kexin Zhang1, Desheng Zheng1, *

1 School of Computer Science, Research Center for Cyber Security, Southwest Petroleum University, Chengdu, 610500, China.

* Corresponding Author: Desheng Zheng. Email: email.

Journal on Artificial Intelligence 2020, 2(1), 17-27.


Deep learning has made a great breakthrough in the field of speech and image recognition. Mature deep learning neural network has completely changed the field of nat ural language processing (NLP). Due to the enormous amount of data and opinions being produced, shared and transferred everyday across the Internet and other media, sentiment analysis has become one of the most active research fields in natural language processing. This paper introduces three deep learning networks applied in IMDB movie reviews sent iment analysis. Dataset was divided to 50% positive reviews and 50% negative reviews. Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) neural networ ks are two main types, which are widely used in NLP tasks, while Convolutional Neural Networks (CNN) is often used in image recognition. The results have shown that, CNN n etwork model can achieve good classification effect when applied to sentiment analysis o f movie reviews. CNN have reported the accuracy of 88.22%, while RNN and LSTM hav e reported accuracy of 68.64% and 85.32% respectively.


Cite This Article

APA Style
Cen, P., Zhang, K., Zheng, D. (2020). Sentiment analysis using deep learning approach. Journal on Artificial Intelligence, 2(1), 17-27.
Vancouver Style
Cen P, Zhang K, Zheng D. Sentiment analysis using deep learning approach. J Artif Intell . 2020;2(1):17-27
IEEE Style
P. Cen, K. Zhang, and D. Zheng "Sentiment Analysis Using Deep Learning Approach," J. Artif. Intell. , vol. 2, no. 1, pp. 17-27. 2020.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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