Sentiment analysis, a pivotal area within natural language processing, has witnessed significant advancements with the advent of deep learning methodologies. This survey provides a comprehensive overview of the state-of-the-art in sentiment analysis, focusing specifically on the application of deep learning techniques. The aim is to present a thorough exploration of the existing literature, methodologies, and challenges associated with leveraging deep neural networks for sentiment analysis tasks.

Text analysis, Natural language processing, sentiment analysis, prediction, machine learning, and random forests.

[1] Liu B. Sentiment analysis: mining opinions, sentiments, and emotions. The Cambridge University Press, 2015.
[2] Liu B. Sentiment analysis and opinion mining (introduction and survey), Morgan & Claypool, May 2012.
[3] Pang B and Lee L. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1–2): pp.1–135, 2008.
[4] Goodfellow I, Bengio Y, Courville A. Deep learning. The MIT Press. 2016.
[5] Nirmala vargesh babu,E grace Mary ganga. Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review Springer, 2022.
[6] Avishek Garain, Sainik Kumar Mahata. Sentiment Analysis at SEPLN (TASS)-2019: Sentiment Analysis at Tweet level using Deep Learning, 2019.
[7] Graph Convolutional Networks for Text Classification Liang Yao, Chengsheng Mao, Yuan Luo*. The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI-19, 2019.
[8] Abdalraouf Hassan and Ausif Mamhmood, “Convolutional recurrent deep learning model for sentence classification,” in IEEE Access, Vol.6, pp.13949-13957, 2018.
[9] Z. Jianqiang, G. Xiaolin and Z. Xuejun, "Deep convolution neural networks for twitter sentiment analysis," in IEEE Access, Vol.6, pp.23253-23260, 2018.
[10] Z. Z. Wint, Y. Manabe and M. Aritsugi, "Deep learning based sentiment classification in social network services datasets," 2018 IEEE International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD), Yonago, pp.91-96, 2018.
[11] K. Baktha and B. K. Tripathy, "Investigation of recurrent neural networks in the field of sentiment analysis," 2017 International Conference on Communication and Signal Processing (ICCSP), Chennai, pp.2047-2050, 2017.
[12] Liu P, Qiu X, Huang X (2016) Recurrent neural network for text classification with multi-task learning. arXiv preprint arXiv: 16050 5101, 2016.
[13] Zhou X, Wan X, Xiao J. Attention-based LSTM network for cross-lingual sentiment classification. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2016.
[14] Tai K.S, Socher R, Manning C. D. Improved semantic representations from tree-structured long short-term memory networks. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL 2015), 2015.
[15] Cho K, Bahdanau D, Bougares F, Schwenk H and Bengio Y. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2014), 2014.
[16] Zhou, Chunting & Sun, Chonglin & Liu, Zhiyuan & Lau, Francis. “A C-LSTM neural network for text classification”, 2015.
[17] J. Panthati, J. Bhaskar, T. K. Ranga and M. R. Challa, "Sentiment analysis of product reviews using deep learning," 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Bangalore 2018, pp.2408-2414, 2018.
[18] Adyan Marendra Ramadhani and Hong Soon Goo, “Twitter sentiment analysis using deep learning methods,” 7th International Annual Engineering Seminar (InAES), Indonesia, 2017.
[19] Kim, Y., Convolutional neural networks for sentence classification, 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, pp.1746–1751, 2014.