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Deep Learning Application to Ensemble Learning—The Simple, but Effective, Approach to Sentiment Classifying

1
Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology, VNU-HCM, Ho Chi Minh City, Vietnam
2
Faculty of Information Technology, Ho Chi Minh City University of Foreign Languages and Information Technology (HUFLIT), Ho Chi Minh City, Vietnam
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(13), 2760; https://doi.org/10.3390/app9132760
Received: 5 June 2019 / Revised: 21 June 2019 / Accepted: 22 June 2019 / Published: 8 July 2019
(This article belongs to the Section Computing and Artificial Intelligence)
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Abstract

Sentiment analysis is an active research area in natural language processing. The task aims at identifying, extracting, and classifying sentiments from user texts in post blogs, product reviews, or social networks. In this paper, the ensemble learning model of sentiment classification is presented, also known as CEM (classifier ensemble model). The model contains various data feature types, including language features, sentiment shifting, and statistical techniques. A deep learning model is adopted with word embedding representation to address explicit, implicit, and abstract sentiment factors in textual data. The experiments conducted based on different real datasets found that our sentiment classification system is better than traditional machine learning techniques, such as Support Vector Machines and other ensemble learning systems, as well as the deep learning model, Long Short-Term Memory network, which has shown state-of-the-art results for sentiment analysis in almost corpuses. Our model’s distinguishing point consists in its effective application to different languages and different domains. View Full-Text
Keywords: sentiment analysis; ensemble learning; deep learning; CEM; deep features; surface features; valence shifters sentiment analysis; ensemble learning; deep learning; CEM; deep features; surface features; valence shifters
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Khai Tran, T.; Thi Phan, T. Deep Learning Application to Ensemble Learning—The Simple, but Effective, Approach to Sentiment Classifying. Appl. Sci. 2019, 9, 2760.

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