Open Access

ARTICLE

Sentiment Analysis Using Deep Learning Approach

Peng Cen¹, Kexin Zhang¹, Desheng Zheng^{1, *}

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

* Corresponding Author: Desheng Zheng. Email: .

Journal on Artificial Intelligence 2020, 2(1), 17-27. https://doi.org/10.32604/jai.2020.010132

Received 13 February 2020; Accepted 01 April 2020; Issue published 15 July 2020

Abstract

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.

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Citations