A Survey of Sentiment Analysis Based on Deep Learning
Sentiment analysis is a very active research topic.
Every day, Facebook, Twitter, Weibo, and other social media,
as well as significant e-commerce websites, generate a massive
amount of comments, which can be used to analyse peoples
opinions or emotions. The existing methods for sentiment analysis
are based mainly on sentiment dictionaries, machine learning, and
deep learning. The first two kinds of methods rely on heavily
sentiment dictionaries or large amounts of labelled data. The third
one overcomes these two problems. So, in this paper, we focus
on the third one. Specifically, we survey various sentiment analysis
methods based on convolutional neural network, recurrent neural
network, long short-term memory, deep neural network, deep belief
network, and memory network. We compare their futures, advantages,
and disadvantages. Also, we point out the main problems of
these methods, which may be worthy of careful studies in the
future. Finally, we also examine the application of deep learning in
multimodal sentiment analysis and aspect-level sentiment analysis.
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[1] K. Coffman and A. Odlyzko, Internet Growth: Is there a “Moore’s law”
for data traffic? Handbook of Massive Data Sets, 2002, pp. 47–93.
[2] S. F. Pengnate and F. J. Riggins, “The role of emotion in p2p
microfinance funding: A sentiment analysis approach,” International
Journal of Information Management, vol. 54, p. 102138, 2020.
[3] C.-M. Yu, “Mining opinions from product review: Principles and
algorithm analysis,” Information Studies: Theory & Application, vol. 32,
no. 7, pp. 124–128, 2009, (In Chinese).
[4] B. Liu and L. Zhang, A Survey of opinion mining and sentiment analysis.
Mining Text Data, 2012, pp. 415–463.
[5] T. Nasukawa and J. Yi, “Sentiment analysis: Capturing favorability using
natural language processing,” in Proceedings of the 2nd International
Conference on Knowledge Capture, 2003, pp. 70–77. [6] B. Pang and L. Lee, “Opinion mining and sentiment analysis,”
Foundations and Trends in Information Retrieval, vol. 2, no. 1-2, pp.
1–135, 2008.
[7] B. Liu, “Sentiment analysis and opinion mining,” Synthesis lectures on
human language technologies, vol. 5, no. 1, pp. 1–167, 2012.
[8] C. Alfaro, J. Cano-Montero, J. Gomez, J. M. Moguerza, and F. Ortega,
“A multi-stage method for content classification and opinion mining on
weblog comments,” Annals of Operations Research, vol. 236, no. 1, pp.
197–213, 2016.
[9] L. Zhang, S. Wang, and B. Liu, “Deep learning for sentiment analysis:
A survey,” Data Mining and Knowledge Discovery, vol. 8, no. 4, pp.
1–25, 2018.
[10] I. Prabha M and G. Umarani Srikanth, “Survey of sentiment
analysis using deep learning techniques,” in Proceedings of the
1st International Conference on Innovations in Information and
Communication Technology, 2019, pp. 1–9.
[11] M. Soleymani, D. Garcia, B. Jou, B. Schuller, S.-F. Chang, and
M. Pantic, “A survey of multimodal sentiment analysis,” Image and
Vision Computing, vol. 65, pp. 3–14, 2017.
[12] D. Stojanovski, G. Strezoski, G. Madjarov, and et al., “Deep neural
network architecture for sentiment analysis and emotion identification
of twitter messages,” Multimedia Tools Applications, vol. 77, no. 24, pp.
32 213–32 242, 2018.
[13] X.-L. Yang, S.-J. Xu, H. Wu, and R.-F. Bie, “Sentiment analysis of
weibo comment texts based on extended vocabulary and convolutional
neural network,” in Proceedings of the 2018 International Conference
on Identification, Information and Knowledge in the Internet of Things,
2018, pp. 9.361–368.
[14] H.-Y. He, J. Zheng, and Z.-P. Zhang, “Text sentiment analysis combined
with part of speech features and convolutional neural network,”
Computer Engineering, vol. 44, no. 11, pp. 209–214, 2018.
[15] Z.-F. Sun and J. Wang, “Rcnn-bgru-hn network model for aspect-based
sentiment analysis,” Computer Science, vol. 46, no. 9, pp. 223–228,
2018, (In Chinese).
[16] S. Chen, Y. Ding, Z. Xie, S. Liu, and H. Ding, “Chinese Weibo
sentiment analysis based on character embedding with dual-channel
convolutional neural network,” in Proceedings of 2018 IEEE 3rd
International Conference on Cloud Computing and Big Data Analysis,
2018, pp. 107–111.
[17] A. Shenoy and A. Sardana, “Multilogue-net: A context aware rnn for
multi-modal emotion detection and sentiment analysis in conversation,”
in Proceedings of the 2020 Computing Research Repository, 2020, pp.
1–9.
[18] Z.-F. Hao, H. Huang, R.-C. Cai, and W. Wen, “Fine-grained opinion
analysis based on multi-feature fusion and bidirectional RNN,”
Computer Engineering, vol. 44, no. 7, pp. 199–2049, 2018, (In Chinese).
[19] S.-W. Pei and L.-L. Wang, “Text sentiment analysis based on attention
mechanism,” Computer Engineering & Science, vol. 41, no. 02, pp.
343–353, 2019, (In Chinese).
[20] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural
computation, vol. 9, no. 8, p. 17351780, 1997.
[21] G.-H. Chen, “Text sentiment analysis based on polarity transfer and
bidirectional long-short term memory,” Information Technology, no. 2,
pp. 149–152, 2018.
[22] H.-Y. Peng, L. Xu, L.-D. Bing, F. Huang, W. Lu, and L. Si, “Knowing
what, how and why: A near complete solution for aspect-based sentiment
analysis,” in Proceedings of the Thirty-Fourth AAAI Conference on
Artificial Intelligence, 2020, pp. 1–9.
[23] Y. Tay, L.-A. Tuan, and S.-C. Hui, “Learning to attend via word-aspect
associative fusion for aspect-based sentiment analysis,” in Proceedings
of the Thirty-Second AAAI Conference on Artificial Intelligence, vol. 18,
2018, pp. 5956–5963.
[24] Z. Xu, B. Liu, B. Wang, S. C., and X. Wang, “Incorporating
loose-structured knowledge into lstm with recall gate for conversation
modeling,” in Proceedings of the 2016 Computing Research Repository,
2016, pp. 1–10.
[25] Y.-K. Ma, H.-Y. Peng, and E. Cambria, “Targeted aspect-based sentiment
analysis via embedding commonsense knowledge into an attentive lstm,”
in Proceedings of the Thirty-Second AAAI Conference on Artificial
Intelligence, 2018, pp. 5876–5883.
[26] B.-W. Xing, L.-J. Liao, D.-D. Song, J.-G. Wang, F.-Z. Zhang, and
H.-Y. Huang, “Earlier attention? aspect-aware lstm for aspect-based
sentiment analysis,” in Proceedings of the Twenty-Eighth International
Joint Conference on Artificial Intelligence, 2019, pp. 5313–5319.
[27] Y. Wang, Q. Chen, M. Ahmed, Z. Li, W. Pan, and H. Liu, “Joint
inference for aspect-level sentiment analysis by deep neural networks
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@article{"International Journal of Information, Control and Computer Sciences:80038", author = "Pingping Lin and Xudong Luo and Yifan Fan", title = "A Survey of Sentiment Analysis Based on Deep Learning", abstract = "Sentiment analysis is a very active research topic.
Every day, Facebook, Twitter, Weibo, and other social media,
as well as significant e-commerce websites, generate a massive
amount of comments, which can be used to analyse peoples
opinions or emotions. The existing methods for sentiment analysis
are based mainly on sentiment dictionaries, machine learning, and
deep learning. The first two kinds of methods rely on heavily
sentiment dictionaries or large amounts of labelled data. The third
one overcomes these two problems. So, in this paper, we focus
on the third one. Specifically, we survey various sentiment analysis
methods based on convolutional neural network, recurrent neural
network, long short-term memory, deep neural network, deep belief
network, and memory network. We compare their futures, advantages,
and disadvantages. Also, we point out the main problems of
these methods, which may be worthy of careful studies in the
future. Finally, we also examine the application of deep learning in
multimodal sentiment analysis and aspect-level sentiment analysis.", keywords = "Natural language processing, sentiment analysis,
document analysis, multimodal sentiment analysis, deep learning.", volume = "14", number = "12", pages = "473-13", }
