Deep Learning Based Techniques for Sentiment Analysis: A Survey
The automated representation of human language using a number of techniques is called Natural Language Processing (NLP). Improvements to NLP applications are important and can be done using a variety of methods, including graphs, deep neural networks, and word embedding. Sentiment classification, which attempts to automatically classify opinionated text as positive, negative, or neutral, is a fundamental activity of sentiment analysis. Sentiment analysis methods focused on deep learning over the past five years are analyzed in this review
G. Beigi, X. Hu, R. Maciejewski, and H. Liu, “An Overview of Sentiment Analysis in Social Media and Its Applications in Disaster Relief,” in Sentiment Analysis and Ontology Engineering, Springer, Cham, 2016, pp. 313–340.
M. Biltawi, W. Etaiwi, S. Tedmori, A. Hudaib, and A. Awajan, “Sentiment classification techniques for Arabic language: A survey,” 2016 7th Int. Conf. Inf. Commun. Syst. ICICS 2016, pp. 339–346, 2016.
V. Rajput and S. Dubey, “An Overview of Use of Natural Language Processing in Sentiment Analysis based on User Opinions,” Int. J. Adv. Res. Comput. Sci. Softw. Eng., vol. 6, no. 4, 2016.
M. M. Lopez and J. Kalita, “Deep Learning applied to NLP,” CoRR, vol. abs/1703.03091, 2017.
L. Deng, “Deep Learning: Methods and Applications,” Found. Trends® Signal Process., vol. 7, no. 3–4, pp. 197–387, 2014, doi: 10.1561/2000000039.
T. Young, D. Hazarika, S. Poria, and E. Cambria, “Recent trends in deep learning based natural language processing,” Ieee Comput. Intell. Mag., vol. 13, no. 3, pp. 55–75, 2018.
L. Deng and Y. Liu, Eds., Deep Learning in Natural Language Processing. Springer Singapore, 2018.
A. Altaher, “Hybrid approach for sentiment analysis of Arabic tweets based on deep learning model and features weighting,” Int. J. Adv. Appl. Sci., vol. 4, no. 8, pp. 43–49, Aug. 2017, doi: 10.21833/ijaas.2017.08.007.
Y. LeCun, Y. Bengio, and others, “Convolutional networks for images, speech, and time series,” Handb. Brain Theory Neural Netw., vol. 3361, no. 10, p. 1995, 1995.
Y. Gao, W. Rong, Y. Shen, and Z. Xiong, “Convolutional Neural Network based sentiment analysis using Adaboost combination,” in 2016 International Joint Conference on Neural Networks (IJCNN), Jul. 2016, pp. 1333–1338, doi: 10.1109/IJCNN.2016.7727352.
G. Cai and B. Xia, “Convolutional Neural Networks for Multimedia Sentiment Analysis,” in Natural Language Processing and Chinese Computing, Springer, Cham, 2015, pp. 159–167.
S. Rani and P. Kumar, “Deep Learning Based Sentiment Analysis Using Convolution Neural Network,” Arab. J. Sci. Eng., vol. 44, no. 4, pp. 3305–3314, Aug. 2018, doi: 10.1007/s13369-018-3500-z.
J. Kapociute-Dzikiene, R. Damaševičius, and M. Woźniak, “Sentiment Analysis of Lithuanian Texts Using Traditional and Deep Learning Approaches,” Comput., vol. 8, p. 4, 2019.
A. Kumar, K. Srinivasan, W.-H. Cheng, and A. Y. Zomaya, “Hybrid context enriched deep learning model for fine-grained sentiment analysis in textual and visual semiotic modality social data,” Inf. Process. Manag., vol. 57, no. 1, p. 102141, 2020, doi: https://doi.org/10.1016/j.ipm.2019.102141.
P. Le and W. Zuidema, “Quantifying the Vanishing Gradient and Long Distance Dependency Problem in Recursive Neural Networks and Recursive LSTMs,” in Proceedings of the 1st Workshop on Representation Learning for NLP, Berlin, Germany, Aug. 2016, pp. 87–93, doi: 10.18653/v1/W16-1610.
J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Netw., vol. 61, pp. 85–117, Jan. 2015, doi: 10.1016/j.neunet.2014.09.003.
M. Schuster and K. K. Paliwal, “Bidirectional Recurrent Neural Networks,” Trans Sig Proc, vol. 45, no. 11, pp. 2673–2681, Nov. 1997, doi: 10.1109/78.650093.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997.
K. Cho, B. van Merriënboer, D. Bahdanau, and Y. Bengio, “On the Properties of Neural Machine Translation: Encoder–Decoder Approaches,” in Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, Doha, Qatar, Oct. 2014, pp. 103–111, doi: 10.3115/v1/W14-4012.
K. Baktha and B. K. Tripathy, “Investigation of recurrent neural networks in the field of sentiment analysis,” in 2017 International Conference on Communication and Signal Processing (ICCSP), Apr. 2017, pp. 2047–2050, doi: 10.1109/ICCSP.2017.8286763.
S. E., L. Yang, M. Zhang, and Y. Xiang, “Aspect-based Financial Sentiment Analysis with Deep Neural Networks,” in Companion Proceedings of the The Web Conference 2018, Republic and Canton of Geneva, Switzerland, 2018, pp. 1951–1954, doi: 10.1145/3184558.3191825.
G. Piao and J. G. Breslin, “Financial Aspect and Sentiment Predictions with Deep Neural Networks: An Ensemble Approach,” in Companion of the The Web Conference 2018 on The Web Conference 2018, 2018, pp. 1973–1977.
L.-C. Chen, C.-M. Lee, and M.-Y. Chen, “Exploration of social media for sentiment analysis using deep learning,” Soft Comput., vol. 24, no. 11, pp. 8187–8197, Oct. 2019, doi: 10.1007/s00500-019-04402-8.
H. Ghulam, F. Zeng, W. Li, and Y. Xiao, “Deep Learning-Based Sentiment Analysis for Roman Urdu Text,” Procedia Comput. Sci., vol. 147, pp. 131–135, 2019, doi: https://doi.org/10.1016/j.procs.2019.01.202.
D. Tang, “Sentiment-Specific Representation Learning for Document-Level Sentiment Analysis,” in Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, New York, NY, USA, 2015, pp. 447–452, doi: 10.1145/2684822.2697035.
K. Chakraborty, S. Bhatia, S. Bhattacharyya, J. Platos, R. Bag, and A. E. Hassanien, “Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media,” Appl. Soft Comput., vol. 97, p. 106754, 2020, doi: https://doi.org/10.1016/j.asoc.2020.106754.
A. Al Sallab, H. Hajj, G. Badaro, R. Baly, W. El Hajj, and K. B. Shaban, “Deep learning models for sentiment analysis in Arabic,” in Proceedings of the Second Workshop on Arabic Natural Language Processing, 2015, pp. 9–17.
This work is licensed under a Creative Commons Attribution 3.0 License.