Abstract
Today’s world is the word of the internet and word of information. In this modern area of development, technology has contributed a lot to the global platform called Web. Reviews and emotions play a vital role in our day to day lives as they help in learning communication, decision making, product evaluation, election prediction. Artificial Intelligence (AI) is the branch of computer science that has worked on the analysis of the reviews as well as opinions generated by the people, and helps the media in order to cope with the situation. Currently to improve the marketing strategy and product advertisement traditional web-based survey methods have been replaced with the Sentiment Analysis which improves customer service. Therefore, various approaches such as machine learning, lexicon-based, hybrid, and other approaches were used to analyze these sentiments/opinions in the past. With the current advancements in deep neural networks, deep learning-based methods are becoming very popular due to their accuracy enhancement in recent times. Various methods like Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bi-LSTM (Bidirectional LSTM) are used for sentiment analysis. This work highlights different deep learning techniques used for text -based sentiment analysis for reviews generated by users.
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References
Tembhurne, J.V., Diwan, T.: Sentiment analysis in textual, visual and multimodal inputs using recurrent neural networks. Multimed. Tools Appl. 80(5), 6871–6910 (2021)
Al-Moslmi, T., Ocaña, M.G., Opdahl, A.L., Veres, C.: Named entity extraction for knowledge graphs: a literature overview. IEEE Access 8, 32862–32881 (2020)
Otter, D.W., Medina, J.R., Kalita, J.K.: A survey of the usages of deep learning for natural language processing. IEEE Trans. Neural Netw. Learn. Syst. 32(2), 604–624 (2020)
Duong, H.T., Nguyen-Thi, T.A.: A review: preprocessing techniques and data augmentation for sentiment analysis. Comput. Soc. Netw. 8(1), 1–16 (2021)
Yadav, A., Vishwakarma, D.K.: Sentiment analysis using deep learning architectures: a review. Artif. Intell. Rev. 53(6), 4335–4385 (2020)
Jain, P.K., Saravanan, V., Pamula, R.: A hybrid CNN-LSTM: a deep learning approach for consumer sentiment analysis using qualitative user-generated contents. Trans. Asian Low-Resour. Lang. Inf. Process. 20(5), 1–15 (2021)
Priyadarshini, I., Cotton, C.: A novel LSTM-CNN-grid search-based deep neural network for sentiment analysis. J. Supercomput. 1–22 (2021)
Basiri, M.E., Nemati, S., Abdar, M., Cambria, E., Acharya, U.R.: ABCDM: an attention-based bidirectional CNN-RNN deep model for sentiment analysis. Futur. Gener. Comput. Syst. 115, 279–294 (2021)
Ali, N.M., Abd El Hamid, M.M., Youssif, A.: Sentiment analysis for movies reviews dataset using deep learning models. Int. J. Data Min. Knowl. Manag. Process. (IJDKP) 9 (2019)
Ligthart, A., Catal, C., Tekinerdogan, B.: Systematic reviews in sentiment analysis: a tertiary study. Artif. Intell. Rev. 1–57 (2021)
Sivakumar, M., Uyyala, S.R.: Aspect-based sentiment analysis of mobile phone reviews using LSTM and fuzzy logic. Int. J. Data Sci. Anal. 12(4), 355–367 (2021)
Xu, G., Meng, Y., Qiu, X., Yu, Z., Wu, X.: Sentiment analysis of comment texts based on BiLSTM. IEEE Access 7, 51522–51532 (2019)
Colón-Ruiz, C., Segura-Bedmar, I.: Comparing deep learning architectures for sentiment analysis on drug reviews. J. Biomed. Inform. 110, 103539 (2020)
Gandhi, U.D., Kumar, P.M., Babu, G.C., Karthick, G.: Sentiment analysis on twitter data by using convolutional neural network (CNN) and long short term memory (LSTM). Wirel. Pers. Commun. 1–10 (2021)
Nemes, L., Kiss, A.: Social media sentiment analysis based on COVID-19. J. Inf. Telecommun. 5(1), 1–15 (2021)
Beseiso, M., Elmousalami, H.: Subword attentive model for Arabic sentiment analysis: a deep learning approach. ACM Trans. Asian Low-Resour. Lang. (2020)
ONAN, A.: Sentiment analysis on massive open online course evaluations: a text mining and deep learning approach. Comput. Appl. Eng. Educ. 29(3), 572–589 (2021)
Wadawadagi, R., Pagi, V.: Sentiment analysis with deep neural networks: comparative study and performance assessment. Artif. Intell. Rev. 53, 6155–6195 (2020)
Salur, M.U., Aydin, I.: A novel hybrid deep learning model for sentiment classification. IEEE Access 8, 58080–58093 (2020)
Kula, S., Choraś, M., Kozik, R., Ksieniewicz, P., Woźniak, M.: Sentiment analysis for fake news detection by means of neural networks. In: International Conference on Computational Science, pp. 653–666. Springer, Cham (2020)
Patel, P., Patel, D., Naik, C.: Sentiment analysis on movie review using deep learning RNN method. In: Intelligent Data Engineering and Analytics, pp. 155–163. Springer, Singapore (2021)
Ni, R., Cao, H.: Sentiment analysis based on GloVe and LSTM-GRU. In: 2020 39th Chinese Control Conference (CCC), pp. 7492–7497. IEEE (2020)
Santur, Y.: Sentiment analysis based on gated recurrent unit. In: 2019 International Artificial Intelligence and Data Processing Symposium (IDAP), pp. 1–5. IEEE (2019)
Birjali, M., Kasri, M., Beni-Hssane, A.: A comprehensive survey on sentiment analysis: approaches, challenges and trends. Knowl.-Based Syst. 107134 (2021)
Jain, P.K., Pamula, R., Srivastava, G.: A systematic literature review on machine learning applications for consumer sentiment analysis using online reviews. Comput. Sci. Rev. 41, 100413 (2021)
Liu, Y., Lu, J., Yang, J., Mao, F.: Sentiment analysis for E-commerce product reviews by deep learning model of Bert-BiGRU-Softmax. Math. Biosci. Eng.: MBE 17(6), 7819–7837 (2020)
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Kadu, S., Joshi, B. (2022). Text-Based Sentiment Analysis Using Deep Learning Techniques. In: Hong, TP., Serrano-Estrada, L., Saxena, A., Biswas, A. (eds) Deep Learning for Social Media Data Analytics. Studies in Big Data, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-031-10869-3_5
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