A Survey on Deep Learning Techniques for Sentiment Analysis
Keywords:
Natural Language Processing, Sentiment Analysis, Deep Learning Classifiers, LSTM, CNN
Abstract
Social media is a rich source of information nowadays. If we look into social media, sentiment analysis is one of the challenging problems. Sentiment analysis is a substantial area of research in the field of Natural Language Processing. This survey paper reviews and provides the comparative study of deep learning approaches CNN, RNN, LSTM and ensemble-based methods.
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[6]. Alwehaibi A, Roy K. Comparison of Pre-Trained Word Vectors for Arabic Text Classification Using Deep Learning Approach. In2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) 2018 Dec 17 (pp. 1471-1474). IEEE.
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[8]. Day MY, Lin YD. Deep Learning for sentiment analysis on google plays consumer review. In2017 IEEE International Conference on Information Reuse and Integration (IRI) 2017 Aug 4 (pp. 382-388). IEEE.
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[10]. Day MY, Lin YD. Deep Learning for sentiment analysis on google plays consumer review. In2017 IEEE International Conference on Information Reuse and Integration (IRI) 2017 Aug 4 (pp. 382-388). IEEE.
[11]. Alshari EM, Azman A, Doraisamy S, Mustapha N, Alkeshr M. Effective Method for Sentiment Lexical Dictionary Enrichment Based on Word2Vec for Sentiment Analysis. In2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP) 2018 Mar 26 (pp. 1-5). IEEE.
[12]. Yang G, He H, Chen Q. Emotion-Semantic-Enhanced Neural Network. IEEE/ACM Transactions on Audio, Speech, and Language Processing. 2019 Mar;27(3):531-43.
[13]. Zhang Z, Lan M. Learning sentiment-inherent word embedding for word-level and sentence-level sentiment analysis. In2015 International Conference on Asian Language Processing (IALP) 2015 Oct 24 (pp. 94-97). IEEE.
[14]. Cai J, Li J, Li W, Wang J. Deep learning Model Used in Text Classification. In2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) 2018 Dec 14 (pp. 123-126). IEEE.
[15]. Zharmagambetov AS, Pak AA. Sentiment analysis of a document using a deep learning approach and decision trees. In2015 Twelve International Conference on Electronics Computer and Computation (ICECCO) 2015 Sep 27 (pp. 1-4). IEEE.
[16]. Bandana R. Sentiment Analysis of Movie Reviews Using Heterogeneous Features. In2018 2nd International Conference on Electronics, Materials Engineering & Nano-Technology (IEMENTech) 2018 May 4 (pp. 1-4). IEEE.
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[23]. Mejora Y. Sentiment analysis: An overview. Comprehensive exam paper. Computer Science Department. 2009:
[24]. Hassan A, Mahmood A. Convolutional recurrent deep learning model for sentence classification. IEEE Access. 2018; 6:13949-57.
[25]. Hassan A, Mahmood A. Deep learning approach for sentiment analysis of short texts. In2017 3rd international conference on control, automation and robotics (ICCAR) 2017 Apr 24 (pp. 705-710). IEEE.
[26]. Yadav, R. K. S. A. S., & Khare, M. B. M. D. An Cost-Effective Euclidean Steiner Tree-based Mechanism for Reducing Latency in Cloud.
Published
2021-06-19
How to Cite
Yadav, H., & Suaib, M. (2021). A Survey on Deep Learning Techniques for Sentiment Analysis. International Journal of Advanced Computer Technology, 10(3), 01-04. Retrieved from https://ijact.org/index.php/ijact/article/view/83
Section
Articles
