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

Applied sciences, 2019-07, Vol.9 (13), p.2760 [Peer Reviewed Journal]

2019. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2076-3417 ;EISSN: 2076-3417 ;DOI: 10.3390/app9132760

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  • Title:
    Deep Learning Application to Ensemble Learning—The Simple, but Effective, Approach to Sentiment Classifying
  • Author: Tran, Thien Khai ; Tuoi Thi Phan
  • Subjects: Algorithms ; Artificial intelligence ; CEM ; Classification ; Computer vision ; Data mining ; Datasets ; deep features ; Deep learning ; Domains ; ensemble learning ; Language ; Learning algorithms ; Machine learning ; Natural language processing ; Polarity ; Sentiment analysis ; Speech processing ; surface features ; Teaching methods ; valence shifters
  • Is Part Of: Applied sciences, 2019-07, Vol.9 (13), p.2760
  • Description: 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.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 2076-3417
    EISSN: 2076-3417
    DOI: 10.3390/app9132760
  • Source: Open Access: DOAJ Directory of Open Access Journals
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
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