SpringerLink
Log in

A review of sentiment analysis: tasks, applications, and deep learning techniques

  • Review
  • Published:
International Journal of Data Science and Analytics Aims and scope Submit manuscript

Abstract

Sentiment analysis, a transformative force in natural language processing, revolutionizes diverse fields such as business, social media, healthcare, and disaster response. This review delves into the intricate landscape of sentiment analysis, exploring its significance, challenges, and evolving methodologies. We examine crucial aspects like dataset selection, algorithm choice, language considerations, and emerging sentiment tasks. The suitability of established datasets (e.g., IMDB Movie Reviews, Twitter Sentiment Dataset) and deep learning techniques (e.g., BERT) for sentiment analysis is explored. While sentiment analysis has made significant strides, it faces challenges such as deciphering sarcasm and irony, ensuring ethical use, and adapting to new domains. We emphasize the dynamic nature of sentiment analysis, encouraging further research to unlock the nuances of human sentiment expression and promote responsible and impactful applications across industries and languages.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

Data sharing does not apply to this article as no datasets were generated or analyzed during the current study.

References

  1. Zhang, W., Xu, M., Jiang, Q.: Opinion mining and sentiment analysis in social media: Challenges and applications. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 536–548. Springer, Berlin (2018)

  2. Pang, B., Lee, L.: Opinion mining and sentiment analysis, Foundations and Trends® in Information Retrieval: 2(1–2), 1-135. https://doi.org/10.1561/1500000011

  3. Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-Based Methods for Sentiment Analysis (2011). http://direct.mit.edu/coli/article-pdf/37/2/267/1798865/coli_a_00049.pdf

  4. Salur, M.U., Aydin, I.: A novel hybrid deep learning model for sentiment classification. IEEE Access 8, 58080–58093 (2020). https://doi.org/10.1109/ACCESS.2020.2982538

    Article  Google Scholar 

  5. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding (2018). https://doi.org/10.48550/ar**v.1810.04805

  6. Daniel Ruby.: Twitter Statistics: New Trends, Figures & Data. Demand Sage (2022).

  7. Simon Kemp.: Facebook Statistics and Trends, Datareportal

  8. Tul, Q., et al.: Sentiment analysis using deep learning techniques: a review. Int. J. Adv. Comput. Sci. Appl.Comput. Sci. Appl. (2017). https://doi.org/10.14569/ijacsa.2017.080657

    Article  Google Scholar 

  9. Zhang, L., Wang, S., Liu, B.: Deep learning for sentiment analysis: a survey. WIREs Data Min. Knowl. Discov. (2018). https://doi.org/10.1002/widm.1253

    Article  Google Scholar 

  10. Ravi, K., Ravi, V.: A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. Knowl. Based Syst. 89, 14–46 (2015). https://doi.org/10.1016/j.knosys.2015.06.015

    Article  Google Scholar 

  11. Liu, B., Zhang, L.: A survey of opinion mining and sentiment analysis. In: Mining Text Data. Springer US, Boston, pp. 415–463 (2012). https://doi.org/10.1007/978-1-4614-3223-4_13

  12. Islam, Md.S., et al.: Challenges and future in deep learning for sentiment analysis: a comprehensive review and a proposed novel hybrid approach. Artif. Intell. Rev. Intell. Rev. 57(3), 62 (2024). https://doi.org/10.1007/s10462-023-10651-9

    Article  Google Scholar 

  13. Elsa, J., Koraye, J.: Deep learning techniques for natural language processing: recent developments (2024). https://easychair.org/publications/preprint_download/FPbH

  14. Dang, N.C., Moreno-García, M.N., De la Prieta, F.: Sentiment analysis based on deep learning: a comparative study. Electronics (Basel) 9(3), 483 (2020). https://doi.org/10.3390/electronics9030483

    Article  Google Scholar 

  15. Prabha, M.I., Umarani Srikanth, G.: Survey of sentiment analysis using deep learning techniques. In: 2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT), pp. 1–9 (2019). https://doi.org/10.1109/ICIICT1.2019.8741438

  16. Yadav, A., Vishwakarma, D.K.: Sentiment analysis using deep learning architectures: a review. Artif. Intell. Rev.. Intell. Rev. 53(6), 4335–4385 (2020). https://doi.org/10.1007/s10462-019-09794-5

    Article  Google Scholar 

  17. Ain, Q.T., et al.: Sentiment analysis using deep learning techniques: a review. Int. J. Adv. Comput. Sci. Appl.Comput. Sci. Appl. (2017). https://doi.org/10.14569/IJACSA.2017.080657

    Article  Google Scholar 

  18. Mäntylä, M.V., Graziotin, D., Kuutila, M.: The evolution of sentiment analysis—a review of research topics venues and top cited papers. Comput. Sci. Rev. 27, 16–32 (2018). https://doi.org/10.1016/j.cosrev.2017.10.002

    Article  Google Scholar 

  19. Dolianiti, F.S., Iakovakis, D., Dias, S.B., Hadjileontiadou, S., Diniz, J.A., Hadjileontiadis, L.: Sentiment analysis techniques and applications in education: a survey. In: Communications in Computer and Information Science, pp. 412–427. Springer, Berlin (2019). https://doi.org/10.1007/978-3-030-20954-4_31

  20. Shah, P.V., Swaminarayan, P.R.: Sentiment analysis—an evaluation of the sentiment of the people: a survey. In: Kotecha, K., Piuri, V., Shah, H.N., Patel, R. (eds.) Data Science and Intelligent Applications, pp. 53–61. Springer, Singapore (2021)

    Chapter  Google Scholar 

  21. Behdenna, S., Barigou, F., Belalem, G.: Document level sentiment analysis: a survey. EAI Endors. Trans. Context-Aware Syst. Appl. 4(13), e2–e2 (2018)

    Google Scholar 

  22. Meena, A., Prabhakar, T.V.: Sentence level sentiment analysis in the presence of conjuncts using linguistic analysis. In: Advances in Information Retrieval: 29th European Conference on IR Research, ECIR 2007, Rome, Italy, April 2–5, 2007. Proceedings 29, pp. 573–580 (2007)

  23. Mukherjee, A., Liu, B.: Aspect extraction through semi-supervised modeling. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, Long Papers, vol. 1, pp. 339–348 (2012)

  24. Alm, C.O., Roth, D., Sproat, R.: Emotions from text: machine learning for text-based emotion prediction. In: HLT (2005)

  25. Dragoni, M., Petrucci, G.: A neural word embeddings approach for multi-domain sentiment analysis. IEEE Trans. Affect. Comput.Comput. 8(4), 457–470 (2017). https://doi.org/10.1109/TAFFC.2017.2717879

    Article  Google Scholar 

  26. Yuan, Z., Wu, S., Wu, F., Liu, J., Huang, Y.: Domain attention model for multi-domain sentiment classification. Knowl. Based Syst. 155, 1–10 (2018). https://doi.org/10.1016/j.knosys.2018.05.004

    Article  Google Scholar 

  27. Boiy, E., Moens, M.-F.: A machine learning approach to sentiment analysis in multilingual Web texts. Inf. Retr. Boston 12(5), 526–558 (2009). https://doi.org/10.1007/s10791-008-9070-z

    Article  Google Scholar 

  28. Soleymani, M., Garcia, D., Jou, B., Schuller, B., Chang, S.F., Pantic, M.: A survey of multimodal sentiment analysis. Image Vis. Comput.Comput. 65, 3–14 (2017). https://doi.org/10.1016/j.imavis.2017.08.003

    Article  Google Scholar 

  29. Morency, L.-P., Mihalcea, R., Doshi, P.: Towards multimodal sentiment analysis: harvesting opinions from the web. In: Proceedings of the 13th International Conference on Multimodal Interfaces, in ICMI ’11. New York, NY, USA: Association for Computing Machinery, pp. 169–176 (2011). https://doi.org/10.1145/2070481.2070509.

  30. Moussa, M.E., Mohamed, E.H., Haggag, M.H.: A survey on opinion summarization techniques for social media. Future Comput. Inform. J. 3(1), 82–109 (2018). https://doi.org/10.1016/j.fcij.2017.12.002

    Article  Google Scholar 

  31. Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, in KDD’ 04. Association for Computing Machinery, New York, NY, USA, pp. 168–177 (2004). https://doi.org/10.1145/1014052.1014073

  32. **dal, N., Liu, B.: Opinion spam and analysis. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, in WSDM ’08. Association for Computing Machinery, New York, NY, USA, pp. 219–230 (2008). https://doi.org/10.1145/1341531.1341560

  33. Kim, S.-M., Hovy, E.: Determining the sentiment of opinions. In: Proceedings of the 20th International Conference on Computational Linguistics, in COLING ’04. Association for Computational Linguistics, USA, p. 1367 (2004). https://doi.org/10.3115/1220355.1220555

  34. Ortis, A., Farinella, G.M., Battiato, S.: Survey on visual sentiment analysis. IET Image Process. 14(8), 1440–1456 (2020)

    Article  Google Scholar 

  35. Habimana, O., Li, Y., Li, R., Gu, X., Yu, G.: Sentiment analysis using deep learning approaches: an overview. Sci. China Inf. Sci. (2020). https://doi.org/10.1007/s11432-018-9941-6

    Article  Google Scholar 

  36. Jain, S., Gupta, V.: Sentiment analysis: a recent survey with applications and a proposed ensemble algorithm. In: Smart Innovation, Systems and Technologies, Springer Science and Business Media Deutschland GmbH, pp. 13–25 (2022). https://doi.org/10.1007/978-981-16-9447-9_2

  37. Gottipati, S., Shankararaman, V., Gan, S.: A conceptual framework for analyzing students’ feedback. In: 2017 IEEE Frontiers in Education Conference (FIE), pp. 1–8 (2017). https://doi.org/10.1109/FIE.2017.8190703

  38. Dhanalakshmi, V., Bino, D., Saravanan, A.M.: Opinion mining from student feedback data using supervised learning algorithms. In: 2016 3rd MEC International Conference on Big Data and Smart City (ICBDSC), pp. 1–5 (2016). https://doi.org/10.1109/ICBDSC.2016.7460390

  39. Koufakou, A., Gosselin, J., Guo, D.: Using data mining to extract knowledge from student evaluation comments in undergraduate courses. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 3138–3142 (2016). https://doi.org/10.1109/IJCNN.2016.7727599

  40. Altrabsheh, N., Gaber, M.M., Cocea, M.: SA-E: sentiment analysis for education. Frontiers in Artificial Intelligence and Applications, 255, pp. 353–362 (2013). https://doi.org/10.3233/978-1-61499-264-6-353

  41. Colace, F., de Santo, M., Greco, L.: SAFE: a sentiment analysis framework for E-learning. Int. J. Emerg. Technol. Learn. (iJET) 9(6), 37 (2014). https://doi.org/10.3991/ijet.v9i6.4110

    Article  Google Scholar 

  42. Rani, S., Kumar, P.: A sentiment analysis system to improve teaching and learning. Computer (Long Beach Calif) 50(5), 36–43 (2017). https://doi.org/10.1109/MC.2017.133

    Article  Google Scholar 

  43. Scaffidi, C.: Mining online forums for valuable contributions. In: 2016 11th Iberian Conference on Information Systems and Technologies (CISTI), pp. 1–6 (2016). https://doi.org/10.1109/CISTI.2016.7521559

  44. Cummins, S., Burd, L., Hatch, A.: Using feedback tags and sentiment analysis to generate sharable learning resources investigating automated sentiment analysis of feedback tags in a programming course. In: 2010 10th IEEE International Conference on Advanced Learning Technologies, pp. 653–657 (2010) https://doi.org/10.1109/ICALT.2010.186

  45. Wen, M., Yang, D., Rosé, C.P.: Sentiment analysis in MOOC discussion forums: what does it tell us? In: EDM (2014)

  46. Tucker, C., Pursel, B., Divinsky, A.: Mining student-generated textual data in MOOCS and quantifying their effects on student performance and learning outcomes. In: 2014 ASEE Annual Conference & Exposition Proceedings, ASEE Conferences, pp. 24.907.1–24.907.14. https://doi.org/10.18260/1-2--22840

  47. Oliveiar, L., Figueira, A.: Visualization of sentiment spread on social networked content: Learning analytics for integrated learning environments in. IEEE Glob. Eng. Educ. Conf. (EDUCON) 2017, 1290–1298 (2017). https://doi.org/10.1109/EDUCON.2017.7943014

    Article  Google Scholar 

  48. Chaturvedi, S., Mishra, V., Mishra, N.: Sentiment analysis using machine learning for business intelligence. In: 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), pp. 2162–2166 (2017). https://doi.org/10.1109/ICPCSI.2017.8392100

  49. Benedetto, F., Tedeschi, A.: Big data sentiment analysis for brand monitoring in social media streams by cloud computing. In: Pedrycz, W., Chen, S.-M. (eds.) Sentiment Analysis and Ontology Engineering: An Environment of Computational Intelligence, pp. 341–377. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-30319-2_14

    Chapter  Google Scholar 

  50. Cheng, L.-C., Tsai, S.-L.: Deep learning for automated sentiment analysis of social media. In: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, in ASONAM ’19. Association for Computing Machinery, New York, NY, USA, pp. 1001–1004 (2019). https://doi.org/10.1145/3341161.3344821

  51. Nemes, L., Kiss, A.: Social media sentiment analysis based on COVID-19. J. Inf. Telecommun. 5(1), 1–15 (2021). https://doi.org/10.1080/24751839.2020.1790793

    Article  Google Scholar 

  52. Bhat, M., Qadri, M., Beg, N.-A., Kundroo, M., Ahanger, N., Agarwal, B.: Sentiment analysis of social media response on the Covid19 outbreak. Brain Behav. Immun.Behav. Immun. 87, 136–137 (2020). https://doi.org/10.1016/j.bbi.2020.05.006

    Article  Google Scholar 

  53. Ashok, M., Rajanna, S., Joshi, P.V., Kamath, S.: A personalized recommender system using machine learning based sentiment analysis over social data. In: 2016 IEEE Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), pp. 1–6 (2016). https://doi.org/10.1109/SCEECS.2016.7509354

  54. Sun, J., Wang, G., Cheng, X., Fu, Y.: Mining affective text to improve social media item recommendation. Inf. Process. Manag.Manag. 51(4), 444–457 (2015). https://doi.org/10.1016/j.ipm.2014.09.002

    Article  Google Scholar 

  55. Sousa, M.G., Sakiyama, K., de Souza Rodrigues, L., Moraes, P.H., Fernandes, E.R., Matsubara, E.T.: BERT for stock market sentiment analysis. In: 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), pp. 1597–1601 (2019). https://doi.org/10.1109/ICTAI.2019.00231

  56. Ren, R., Wu, D.D., Liu, T.: Forecasting stock market movement direction using sentiment analysis and support vector machine. IEEE Syst. J. 13(1), 760–770 (2019). https://doi.org/10.1109/JSYST.2018.2794462

    Article  Google Scholar 

  57. Pagolu, V.S., Reddy, K.N., Panda, G., Majhi, B.: Sentiment analysis of Twitter data for predicting stock market movements. In: 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), pp. 1345–1350 (2016). https://doi.org/10.1109/SCOPES.2016.7955659

  58. Khedr, A.E., Salama, S.E., Yaseen, N.: Predicting stock market behavior using data mining technique and news sentiment analysis. Int. J. Intell. Syst. Appl. 9(7), 22–30 (2017). https://doi.org/10.5815/ijisa.2017.07.03

    Article  Google Scholar 

  59. Dang, N.C., Moreno-García, M.N., de la Prieta, F.: Sentiment analysis based on deep learning: a comparative study. Electronics (Switzerland) 9(3), 483 (2020). https://doi.org/10.3390/electronics9030483

    Article  Google Scholar 

  60. Jain, A.P., Dandannavar, P.: Application of machine learning techniques to sentiment analysis. In: 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), pp. 628–632 (2016). https://doi.org/10.1109/ICATCCT.2016.7912076

  61. Chaturvedi, I., Cambria, E., Welsch, R.E., Herrera, F.: Distinguishing between facts and opinions for sentiment analysis: survey and challenges. Inf. Fus. 44, 65–77 (2018). https://doi.org/10.1016/j.inffus.2017.12.006

    Article  Google Scholar 

  62. Rambocas, M., Pacheco, B.G.: Online sentiment analysis in marketing research: a review. J. Res. Interact. Mark. 12(2), 146–163 (2018). https://doi.org/10.1108/JRIM-05-2017-0030

    Article  Google Scholar 

  63. Haselmayer, M., Jenny, M.: Sentiment analysis of political communication: combining a dictionary approach with crowdcoding. Qual. Quant. 51(6), 2623–2646 (2017). https://doi.org/10.1007/s11135-016-0412-4

    Article  Google Scholar 

  64. Kušen, E., Strembeck, M.: Politics, sentiments, and misinformation: an analysis of the Twitter discussion on the 2016 Austrian Presidential Elections. Online Soc. Netw. Media 5, 37–50 (2018). https://doi.org/10.1016/j.osnem.2017.12.002

    Article  Google Scholar 

  65. Kuamri, S., Babu, C.N.: Real time analysis of social media data to understand people emotions towards national parties. In: 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–6 (2017). https://doi.org/10.1109/ICCCNT.2017.8204059

  66. Boukabous, M., Azizi, M.: Crime prediction using a hybrid sentiment analysis approach based on the bidirectional encoder representations from transformers. Indones. J. Electr. Eng. Comput. Sci. 25(2), 1131 (2022). https://doi.org/10.11591/ijeecs.v25.i2.pp1131-1139

    Article  Google Scholar 

  67. el Hannach, H., Benkhalifa, M.: WordNet based implicit aspect sentiment analysis for crime identification from Twitter. Int. J. Adv. Comput. Sci. Appl.Comput. Sci. Appl. (2018). https://doi.org/10.14569/IJACSA.2018.091222

    Article  Google Scholar 

  68. Azeez, J., Aravindhar, D.J.: Hybrid approach to crime prediction using deep learning. In: 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1701–1710. IEEE (2015). https://doi.org/10.1109/ICACCI.2015.7275858

  69. Gerber, M.S.: Predicting crime using Twitter and kernel density estimation. Decis. Support. Syst. Support. Syst. 61, 115–125 (2014). https://doi.org/10.1016/j.dss.2014.02.003

    Article  Google Scholar 

  70. Sufi, F.K., Khalil, I.: Automated disaster monitoring from social media posts using AI-based location intelligence and sentiment analysis. IEEE Trans. Comput. Soc. Syst. (2022). https://doi.org/10.1109/TCSS.2022.3157142

    Article  Google Scholar 

  71. Mendon, S., Dutta, P., Behl, A., Lessmann, S.: A hybrid approach of machine learning and lexicons to sentiment analysis: enhanced insights from Twitter data of natural disasters. Inf. Syst. Front. 23(5), 1145–1168 (2021). https://doi.org/10.1007/s10796-021-10107-x

    Article  Google Scholar 

  72. Maharani, W.: Sentiment analysis during jakarta flood for emergency responses and situational awareness in disaster management using BERT. In: 2020 8th International Conference on Information and Communication Technology (ICoICT), pp. 1–5 (2020). https://doi.org/10.1109/ICoICT49345.2020.9166407

  73. Ragini, J.R., Anand, P.M.R., Bhaskar, V.: Big data analytics for disaster response and recovery through sentiment analysis. Int. J. Inf. Manag. 42, 13–24 (2018). https://doi.org/10.1016/j.i**fomgt.2018.05.004

    Article  Google Scholar 

  74. Singh, P., Sawhney, R.S., Kahlon, K.S.: Sentiment analysis of demonetization of 500 & 1000 rupee banknotes by Indian government. ICT Express 4(3), 124–129 (2018). https://doi.org/10.1016/j.icte.2017.03.001

    Article  Google Scholar 

  75. Roy, K., Kohli, D., Kumar, R.K.S., Sahgal, R., Wen-Bin, Yu.: Sentiment analysis of Twitter data for demonetization in India ? A text mining approach. Issues Inf. Syst. 18(4), 9–15 (2017). https://doi.org/10.48009/4_iis_2017_9-15

    Article  Google Scholar 

  76. Zhang, L., Wang, S., Liu, B.: Deep learning for sentiment analysis: a survey. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. (2018). https://doi.org/10.1002/widm.1253

    Article  Google Scholar 

  77. Mumuni, A., Mumuni, F.: Automated data processing and feature engineering for deep learning and big data applications: a survey. J. Inf. Intell. (2024). https://doi.org/10.1016/j.jiixd.2024.01.002

    Article  Google Scholar 

  78. O’Mahony, N., et al.: Deep learning vs. traditional computer vision. In: Advances in Computer Vision: Proceedings of the 2019 Computer Vision Conference (CVC), vol. 1, pp. 128–144 (2020)

  79. Joseph, J., Vineetha, S., Sobhana, N.V.: A survey on deep learning based sentiment analysis. Mater. Today Proc. 58, 456–460 (2022). https://doi.org/10.1016/j.matpr.2022.02.483

    Article  Google Scholar 

  80. Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998). https://doi.org/10.1109/5.726791

    Article  Google Scholar 

  81. Patterson, J., Gibson, A.: Deep Learning: A Practitioner’s Approach, 1st edn. O’Reilly Media Inc, New York (2017)

    Google Scholar 

  82. Collobert, R., Weston, J.: A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th International Conference on Machine Learning, in ICML ’08. New York, NY, USA: Association for Computing Machinery, pp. 160–167 (2008). https://doi.org/10.1145/1390156.1390177

  83. Wang, J., Sun, J., Lin, H., Dong, H., Zhang, S.: Convolutional neural networks for expert recommendation in community question answering. Sci. China Inf. Sci. 60(11), 110102 (2017). https://doi.org/10.1007/s11432-016-9197-0

    Article  Google Scholar 

  84. Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (Almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)

    Google Scholar 

  85. Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences (2014). ar**v preprint ar**v:1404.2188. https://doi.org/10.48550/ar**v.1404.2188

  86. Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746–1751. Association for Computational Linguistics, Stroudsburg, PA, USA (2014). https://doi.org/10.3115/v1/D14-1181

  87. Johnson, R., Zhang, T.: Deep pyramid convolutional neural networks for text categorization. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Long Papers), vol. 1, pp. 562–570. Association for Computational Linguistics, Stroudsburg, PA, USA (2017). https://doi.org/10.18653/v1/P17-1052

  88. Conneau, A., Schwenk, H., Barrault, L., Lecun, Y.: Very deep convolutional networks for text classification (2016). ar**v preprint ar**v:1606.01781. https://doi.org/10.48550/ar**v.1606.01781

  89. Elman, J.L.: Finding structure in time. Cogn. Sci.. Sci. 14(2), 179–211 (1990). https://doi.org/10.1016/0364-0213(90)90002-E

    Article  Google Scholar 

  90. Ko, W.-J., Tseng, B.-H., Lee, H.-Y.: Recurrent Neural Network based language modeling with controllable external memory. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5705–5709 (2017). https://doi.org/10.1109/ICASSP.2017.7953249

  91. Hochreiter, S.: The vanishing gradient problem during learning recurrent neural nets and problem solutions. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 6(2), 107–116 (1998). https://doi.org/10.1142/S0218488598000094

    Article  MathSciNet  Google Scholar 

  92. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput.Comput. 9(8), 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  93. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y. Learning phrase representations using RNN encoder-decoder for statistical machine translation (2014) ar**v preprint ar**v:1406.1078. https://doi.org/10.48550/ar**v.1406.1078

  94. Sun, X., Li, X., Zhang, S., Wang, S., Wu, F., Li, J., Zhang, T. and Wang, G. Sentiment analysis through llm negotiations (2023) ar**v preprint ar**v:2311.01876. https://doi.org/10.48550/ar**v.2311.01876

  95. Howard, J., Ruder, S.: Universal language model fine-tuning for text classification (2018). ar**v preprint ar**v:1801.06146. https://doi.org/10.48550/ar**v.1801.06146

  96. Khosravi, A., Rahmati, Z., Vefghi, A.: Relational graph convolutional networks for sentiment analysis (2024). ar**v preprint ar**v:2404.13079. https://doi.org/10.48550/ar**v.2404.13079

  97. Marra, G., Diligenti, M., Giannini, F.: Relational reasoning networks (2021). ar**v preprint ar**v:2106.00393. https://doi.org/10.48550/ar**v.2106.00393

  98. Do, H.H., Prasad, P.W.C., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: a comparative review. Expert Syst. Appl. 118, 272–299 (2019). https://doi.org/10.1016/j.eswa.2018.10.003

    Article  Google Scholar 

  99. Lee, G., Jeong, J., Seo, S., Kim, C., Kang, P.: Sentiment classification with word localization based on weakly supervised learning with a convolutional neural network. Knowl. Based Syst. 152, 70–82 (2018). https://doi.org/10.1016/j.knosys.2018.04.006

    Article  Google Scholar 

  100. Dos Santos, C., Gatti, M.: Deep convolutional neural networks for sentiment analysis of short texts. In: COLING (2014)

  101. Hassan, A., Mahmood, A.: Convolutional recurrent deep learning model for sentence classification. IEEE Access 6, 13949–13957 (2018). https://doi.org/10.1109/ACCESS.2018.2814818

    Article  Google Scholar 

  102. Singhal, P. and Bhattacharyya, P., 2016. Sentiment analysis and deep learning: a survey. Center for Indian Language Technology, Indian Institute of Technology, Bombay. https://www.cfilt.iitb.ac.in/~cfiltnew/resources/surveys/sentiment-deeplearning-2016-prerna.pdf

  103. Peng, H., Ma, Y., Li, Y., Cambria, E.: Learning multi-grained aspect target sequence for Chinese sentiment analysis. Knowl. Based Syst. 148, 167–176 (2018). https://doi.org/10.1016/j.knosys.2018.02.034

    Article  Google Scholar 

  104. Rao, G., Huang, W., Feng, Z., Cong, Q.: LSTM with sentence representations for document-level sentiment classification. Neurocomputing 308, 49–57 (2018). https://doi.org/10.1016/j.neucom.2018.04.045

    Article  Google Scholar 

  105. Rana, R.: Gated Recurrent Unit (GRU) for emotion classification from noisy speech (2016). ar**v preprint ar**v:1612.07778. https://doi.org/10.48550/ar**v.1612.07778

  106. Verma, S., Saini, M., Sharan, A.: Deep sequential model for review rating prediction. In: 2017 Tenth International Conference on Contemporary Computing (IC3), pp. 1–6 (2017). https://doi.org/10.1109/IC3.2017.8284318

  107. Acheampong, F.A., Nunoo-Mensah, H., Chen, W.: Transformer models for text-based emotion detection: a review of BERT-based approaches. Artif. Intell. Rev. Intell. Rev. 54(8), 5789–5829 (2021). https://doi.org/10.1007/s10462-021-09958-2

    Article  Google Scholar 

  108. Shojaee-Mend, H., Mohebbati, R., Amiri, M., Atarodi, A.: Evaluating the strengths and weaknesses of large language models in answering neurophysiology questions. Sci. Rep. 14(1), 1–10 (2024)

    Article  Google Scholar 

  109. Lappin, S.: Assessing the strengths and weaknesses of large language models. J. Logic Lang. Inf. 33(1), 9–20 (2024). https://doi.org/10.1007/s10849-023-09409-x

    Article  MathSciNet  Google Scholar 

  110. Chen, T., Qiu, D., Wu, Y., Khan, A., Ke, X., Gao, Y.: View-based explanations for graph neural networks. Proc. ACM Manag. Data (2024). https://doi.org/10.1145/3639295

    Article  Google Scholar 

  111. Liu, X., Zhang, L., Guan, H.: Uplifting message passing neural network with graph original information (2022). ar**v preprint ar**v:2210.05382. https://doi.org/10.48550/ar**v.2210.05382

  112. Jain, P.K., Saravanan, V., Pamula, R.: A hybrid CNN-LSTM: a deep learning approach for consumer sentiment analysis using qualitative user-generated contents. ACM Trans. Asian and Low-Resource Lang. Inf. Process. 20(5), 1 (2021). https://doi.org/10.1145/3457206

    Article  Google Scholar 

  113. Brauwers, G., Frasincar, F.: A survey on aspect-based sentiment classification. ACM Comput. Surv.Comput. Surv. (2021). https://doi.org/10.1145/3503044

    Article  Google Scholar 

  114. Alshuwaier, F., Areshey, A., Poon, J.: Applications and enhancement of document-based sentiment analysis in deep learning methods: systematic literature review. Intell. Syst. Appl. (2022). https://doi.org/10.1016/j.iswa.2022.200090

    Article  Google Scholar 

  115. Rani, S., Kumar, P.: Deep learning based sentiment analysis using convolution neural network. Arab. J. Sci. Eng. 44(4), 3305–3314 (2019). https://doi.org/10.1007/s13369-018-3500-z

    Article  Google Scholar 

  116. Tyagi, V., Kumar, A., Das, S.: Sentiment analysis on twitter data using deep learning approach. In: Proceedings—IEEE 2020 2nd International Conference on Advances in Computing, Communication Control and Networking, ICACCCN 2020, Institute of Electrical and Electronics Engineers Inc., pp. 187–190 (2020). https://doi.org/10.1109/ICACCCN51052.2020.9362853

  117. Yang, L., Li, Y., Wang, J., Sherratt, R.S.: Sentiment analysis for E-commerce product reviews in Chinese based on sentiment lexicon and deep learning. IEEE Access 8, 23522–23530 (2020). https://doi.org/10.1109/ACCESS.2020.2969854

    Article  Google Scholar 

  118. Obiedat, R., Al-Darras, D., Alzaghoul, E., Harfoushi, O.: Arabic aspect-based sentiment analysis: a systematic literature review. In: IEEE Access, Institute of Electrical and Electronics Engineers Inc., vol. 9, pp. 152628–152645 (2021). https://doi.org/10.1109/ACCESS.2021.3127140

  119. Aydln, C.R., Gungor, T.: Combination of recursive and recurrent neural networks for aspect-based sentiment analysis using inter-aspect relations. IEEE Access 8, 77820–77832 (2020). https://doi.org/10.1109/ACCESS.2020.2990306

    Article  Google Scholar 

  120. Gothane, S., et al.: Sentiment analysis in social media using deep learning techniques. IJISAE (2024)

  121. Zyout, I., Zyout, M.: Sentiment analysis of student feedback using attention-based RNN and transformer embedding. Int. J. Artif. Intell. 13(2), 2173–2184 (2024)

    Google Scholar 

  122. Durga, P., Godavarthi, D.: Deep-sentiment: an effective deep sentiment analysis using a decision-based recurrent neural network (D-RNN). IEEE Access 11, 108433–108447 (2023). https://doi.org/10.1109/ACCESS.2023.3320738

    Article  Google Scholar 

  123. Demotte, P., Senevirathne, L., Karunanayake, B., Munasinghe, U., Ranathunga, S.: Sentiment analysis of sinhala news comments using sentence-state LSTM networks. In: 2020 Moratuwa Engineering Research Conference (MERCon), pp. 283–288 (2020). https://doi.org/10.1109/MERCon50084.2020.9185327

  124. Wang, B., Guo, P., Wang, X., He, Y., Wang, W.: Transparent aspect-level sentiment analysis based on dependency syntax analysis and its application on COVID-19. J. Data Inf. Qual. 14(2), 1 (2022). https://doi.org/10.1145/3460002

    Article  Google Scholar 

  125. Sachin, S., Tripathi, A., Mahajan, N., Aggarwal, S., Nagrath, P.: Sentiment analysis using gated recurrent neural networks. SN Comput. Sci. (2020). https://doi.org/10.1007/s42979-020-0076-y

    Article  Google Scholar 

  126. Zhang, B., Li, X., Xu, X., Leung, K.C., Chen, Z., Ye, Y.: Knowledge guided Capsule attention network for aspect-based sentiment analysis. IEEE/ACM Trans. Audio Speech Lang. Process. (2020). https://doi.org/10.1109/TASLP.2020.3017093

    Article  Google Scholar 

  127. Abimbola, B., De La Cal Marin, E., Tan, Q.: Enhancing legal sentiment analysis: a convolutional neural network-long short-term memory document-level model. Mach. Learn Knowl. Extr. 6(2), 877–897 (2024). https://doi.org/10.3390/make6020041

    Article  Google Scholar 

  128. Huang, B., et al.: Aspect-level sentiment analysis with aspect-specific context position information. Knowl. Based Syst. (2022). https://doi.org/10.1016/j.knosys.2022.108473

    Article  Google Scholar 

  129. Sudhir, P., Suresh, V.D.: Comparative study of various approaches, applications and classifiers for sentiment analysis. Glob. Transit. Proc. 2(2), 205–211 (2021). https://doi.org/10.1016/j.gltp.2021.08.004

    Article  Google Scholar 

  130. Rida-E-Fatima, S., et al.: A multi-layer dual attention deep learning model with refined word embeddings for aspect-based sentiment analysis. IEEE Access 7, 114795–114807 (2019). https://doi.org/10.1109/ACCESS.2019.2927281

    Article  Google Scholar 

  131. Zhao, N., Gao, H., Wen, X., Li, H.: Combination of convolutional neural network and gated recurrent unit for aspect-based sentiment analysis. IEEE Access 9, 15561–15569 (2021). https://doi.org/10.1109/ACCESS.2021.3052937

    Article  Google Scholar 

  132. Loh, N.K.N., Lee, C.P., Ong, T.S., Lim, K.M.: MPNet-GRUs: sentiment analysis with masked and permuted pre-training for language understanding and gated recurrent units. IEEE Access (2024). https://doi.org/10.1109/ACCESS.2024.3394930

    Article  Google Scholar 

  133. Zhang, B., Zhou, W.: Transformer-encoder-GRU (T-E-GRU) for Chinese sentiment analysis on Chinese comment text. Neural. Process. Lett. 55, 1847 (2021)

    Article  Google Scholar 

  134. Abdullah, T., Ahmet, A.: Deep learning in sentiment analysis: a survey of recent architectures. ACM Comput. Surv. Comput. Surv. (2022). https://doi.org/10.1145/3548772

    Article  Google Scholar 

  135. Obaidi, M., Nagel, L., Specht, A., Klünder, J.: Sentiment analysis tools in software engineering: a systematic map** study. Inf. Softw. Technol.Softw. Technol. 151, 107018 (2022). https://doi.org/10.1016/j.infsof.2022.107018

    Article  Google Scholar 

  136. Žitnik, S., Blagus, N., Bajec, M.: Target-level sentiment analysis for news articles. Knowl. Based Syst. 249, 108939 (2022). https://doi.org/10.1016/j.knosys.2022.108939

    Article  Google Scholar 

  137. **ao, Y., Li, C., Thürer, M., Liu, Y., Qu, T.: User preference mining based on fine-grained sentiment analysis. J. Retail. Consum. Serv.Consum. Serv. 68, 103013 (2022). https://doi.org/10.1016/j.jretconser.2022.103013

    Article  Google Scholar 

  138. Hartmann, J., Heitmann, M., Siebert, C., Schamp, C.: More than a feeling: accuracy and application of sentiment analysis. Int. J. Res. Mark. (2022). https://doi.org/10.1016/j.ijresmar.2022.05.005

    Article  Google Scholar 

  139. Du, Y., Wang, Y., Wei, K., Jia, J.: The sentiment analysis and sentiment orientation prediction for hotel based on BERT-BiLSTM model. In: Lecture Notes in Electrical Engineering, pp. 498–505. Springer Science and Business Media Deutschland GmbH, Singapore (2022). https://doi.org/10.1007/978-981-16-9423-3_62

  140. Hoang, M., Alija Bihorac, O., Rouces, J.: Aspect-based sentiment analysis using BERT. In Proceedings of the 22nd Nordic conference on computational linguistics (pp. 187-196). https://aclanthology.org/W19-6120

  141. Biswas, E., Karabulut, M.E., Pollock, L., Vijay-Shanker, K.: Achieving reliable sentiment analysis in the software engineering domain using BERT. In: Proceedings—2020 IEEE International Conference on Software Maintenance and Evolution, ICSME 2020, Institute of Electrical and Electronics Engineers Inc., pp. 162–173 (2020). https://doi.org/10.1109/ICSME46990.2020.00025

  142. Chouikhi, H., Chniter, H., Jarray, F.: Arabic sentiment analysis using BERT model. In: Communications in Computer and Information Science, pp. 621–632. Springer Science and Business Media Deutschland GmbH, Singapore (2021). https://doi.org/10.1007/978-3-030-88113-9_50

  143. Acikalin, U.U., Bardak, B., Kutlu, M.: Turkish sentiment analysis using BERT. In: 2020 28th Signal Processing and Communications Applications Conference, SIU 2020—Proceedings, Institute of Electrical and Electronics Engineers Inc. (2020) https://doi.org/10.1109/SIU49456.2020.9302492

  144. Wang, Y., Chen, Q., Wang, W.: Multi-task BERT for aspect-based sentiment analysis. In: Proceedings - 2021 IEEE International Conference on Smart Computing, SMARTCOMP 2021, Institute of Electrical and Electronics Engineers Inc., pp. 383–385 (2021). https://doi.org/10.1109/SMARTCOMP52413.2021.00077

  145. Jafarian, H., Taghavi, A., Javaheri, A., Rawassizadeh, R.: Exploiting Bert To Improve Aspect-Based Sentiment Analysis Performance on Persian Language. https://github.com/hooshvare/parsbert

  146. **e, X., Qin, B., Wan, Z., Nie, W.: Text aspect-level sentiment analysis based on multi- task joint learning. In: Proceedings—2021 2nd International Symposium on Computer Engineering and Intelligent Communications, ISCEIC 2021, Institute of Electrical and Electronics Engineers Inc., pp. 127–131 (2021). https://doi.org/10.1109/ISCEIC53685.2021.00033

  147. Tran, O.T., Bui, V.T.: A BERT-based hierarchical model for vietnamese aspect based sentiment analysis. In: Proceedings—2020 12th International Conference on Knowledge and Systems Engineering, KSE 2020, Institute of Electrical and Electronics Engineers Inc., pp. 269–274 (2020). https://doi.org/10.1109/KSE50997.2020.9287650

  148. Zhang, H., Pan, F., Dong, J., Zhou, Y.: BERT-IAN Model for aspect-based sentiment analysis. In: Proceedings - 2020 International Conference on Communications, Information System and Computer Engineering, CISCE 2020, Institute of Electrical and Electronics Engineers Inc., pp. 250–254 (2020). https://doi.org/10.1109/CISCE50729.2020.00056

  149. Wang, L., Yao, C., Li, X., Yu, X.: BERT-based implicit aspect extraction. In: Proceedings of 2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2021, Institute of Electrical and Electronics Engineers Inc., pp. 758–761 (2021). https://doi.org/10.1109/ICCASIT53235.2021.9633578

  150. Azhar, A.N., Khodra, M.L.: Fine-tuning pretrained multilingual BERT model for indonesian aspect-based sentiment analysis. In: 2020 7th International Conference on Advance Informatics: Concepts, Theory and Applications (ICAICTA), pp. 1–6. IEEE (2020). https://doi.org/10.1109/ICAICTA49861.2020.9428882

  151. dos Santos, B.N., Marcacini, R.M., Rezende, S.O.: Multi-domain aspect extraction using bidirectional encoder representations from transformers. IEEE Access 9, 91604–91613 (2021). https://doi.org/10.1109/ACCESS.2021.3089099

    Article  Google Scholar 

  152. He, A., Abisado, M.: Text sentiment analysis of douban film short comments based on BERT-CNN-BiLSTM-Att model. IEEE Access 12, 45229–45237 (2024). https://doi.org/10.1109/ACCESS.2024.3381515

    Article  Google Scholar 

  153. Miah, M.S.U., Kabir, M.M., Bin Sarwar, T., Safran, M., Alfarhood, S., Mridha, M.F.: A multimodal approach to cross-lingual sentiment analysis with ensemble of transformer and LLM. Sci. Rep. 14(1), 9603 (2024). https://doi.org/10.1038/s41598-024-60210-7

    Article  Google Scholar 

  154. **ng, F.: Designing heterogeneous LLM agents for financial sentiment analysis (2024). ar**v preprint ar**v:2401.05799. https://doi.org/10.48550/ar**v.2401.05799

  155. Zhang, B., Yang, H., Zhou, T., Babar, A., Liu, X.-Y.: Enhancing financial sentiment analysis via retrieval augmented large language models. In Proceedings of the Fourth ACM International Conference on AI in Finance (ICAIF '23). Association for Computing Machinery, New York, NY, USA, 349–356 (2023). https://doi.org/10.1145/3604237.3626866

  156. Deng, X., Bashlovkina, V., Han, F., Baumgartner, S., Bendersky, M.: LLMs to the moon? Reddit market sentiment analysis with large language models. In: Companion Proceedings of the ACM Web Conference 2023, pp. 1014–1019. ACM, New York, NY, USA (2023). https://doi.org/10.1145/3543873.3587605

  157. Krugmann, J.O., Hartmann, J.: Sentiment analysis in the age of generative AI. Cust. Needs Solut. Needs Solut. 11(1), 3 (2024). https://doi.org/10.1007/s40547-024-00143-4

    Article  Google Scholar 

  158. Bhat, R.H.: Stock price trend prediction using emotion analysis of financial headlines with distilled LLM model (2024). https://mavmatrix.uta.edu/cse_theses/4/

  159. Li, Y., Li, N.: Sentiment analysis of weibo comments based on graph neural network. IEEE Access 10, 23497–23510 (2022). https://doi.org/10.1109/ACCESS.2022.3154107

    Article  Google Scholar 

  160. Yang, S., **ng, L., Li, Y., Chang, Z.: Implicit sentiment analysis based on graph attention neural network. Eng. Rep. (2022). https://doi.org/10.1002/eng2.12452

    Article  Google Scholar 

  161. Zhao, X., et al.: RDGCN: reinforced dependency graph convolutional network for aspect-based sentiment analysis. In: Proceedings of the 17th ACM International Conference on Web Search and Data Mining, pp. 976–984. ACM, New York, NY, USA (2024). https://doi.org/10.1145/3616855.3635775

  162. **, Y., Zhao, A.: Bert-based graph unlinked embedding for sentiment analysis. Complex Intell. Syst. 10(2), 2627–2638 (2024). https://doi.org/10.1007/s40747-023-01289-9

    Article  Google Scholar 

  163. Yin, S., Zhong, G.: TextGT: a double-view graph transformer on text for aspect-based sentiment analysis. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 19404–19412 (2024)

  164. Chen, W., Zheng, X., Zhou, H., Li, Z.: Evaluation of logistics service quality: sentiment analysis of comment text based on multi-level graph neural network. Traitement du Signal 38(6), 1853–1860 (2021). https://doi.org/10.18280/ts.380630

    Article  Google Scholar 

  165. Birjali, M., Kasri, M., Beni-Hssane, A.: A comprehensive survey on sentiment analysis: approaches, challenges and trends. Knowl. Based Syst. (2021). https://doi.org/10.1016/j.knosys.2021.107134

    Article  Google Scholar 

  166. 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). https://doi.org/10.1016/j.cosrev.2021.100413

    Article  Google Scholar 

  167. Chakraborty, K., Bhattacharyya, S., Bag, R., Hassanien, A.A.: Sentiment analysis on a set of movie reviews using deep learning techniques. Soc. Netw. Anal. (2019). https://doi.org/10.1016/b978-0-12-815458-8.00007-4

    Article  Google Scholar 

  168. Shayaa, S., et al.: Sentiment analysis of big data: methods, applications, and open challenges. IEEE Access 6, 37807–37827 (2018). https://doi.org/10.1109/ACCESS.2018.2851311

    Article  Google Scholar 

  169. Beseiso, M., Elmousalami, H.: Subword attentive model for arabic sentiment analysis: a deep learning approach. ACM Trans. Asian Low Resource Lang. Inf. Process. 19(2), 1–7 (2020). https://doi.org/10.1145/3360016

    Article  Google Scholar 

  170. Liu, H., Chatterjee, I., Zhou, M., Lu, X.S., Abusorrah, A.: Aspect-based sentiment analysis: a survey of deep learning methods. IEEE Trans. Comput. Soc. Syst. 7(6), 1358–1375 (2020). https://doi.org/10.1109/TCSS.2020.3033302

    Article  Google Scholar 

  171. Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Trans. Knowl. Discov. DataKnowl. Discov. Data (2019). https://doi.org/10.1145/3350487

    Article  Google Scholar 

  172. Liu, L., Chen, H., Sun, Y.: A multi-classification sentiment analysis model of chinese short text based on gated linear units and attention mechanism. ACM Trans. Asian Low-Resource Lang. Inf. Process. (2021). https://doi.org/10.1145/3464425

    Article  Google Scholar 

  173. Agüero-Torales, M.M., Abreu Salas, J.I., López-Herrera, A.G.: Deep learning and multilingual sentiment analysis on social media data: an overview. Appl. Soft Comput.Comput. (2021). https://doi.org/10.1016/j.asoc.2021.107373

    Article  Google Scholar 

  174. Li, Y., Jia, B., Guo, Y., Chen, X.: Mining user reviews for mobile app comparisons. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1(3), 1–15 (2017). https://doi.org/10.1145/3130935

    Article  Google Scholar 

  175. Sehgal, D., Agarwal, A.K.: Real-time sentiment analysis of big data applications using twitter data with Hadoop framework. In: Advances in Intelligent Systems and Computing, pp. 765–772. Springer Verlag, Singapore (2018). https://doi.org/10.1007/978-981-10-5699-4_72

  176. Nasreen Taj, M.B., Girisha, G.S.: Insights of strength and weakness of evolving methodologies of sentiment analysis. Glob. Transit. Proc. 2(2), 157–162 (2021). https://doi.org/10.1016/j.gltp.2021.08.059

    Article  Google Scholar 

  177. Karampatsis, R.-M., Babii, H., Robbes, R., Sutton, C., Janes, A.: Big code != big vocabulary: open-vocabulary models for source code (2020). https://doi.org/10.1145/3377811.3380342

  178. She, J., Hu, Y., Shi, H., Wang, J., Shen, Q., Mei, T.: Dive into Ambiguity: latent distribution mining and pairwise uncertainty estimation for facial expression recognition (2021). https://doi.org/10.48550/ar**v.2104.00232

  179. Li, X., et al.: OSLNet: deep small-sample classification with an orthogonal softmax layer. EEE Trans. Image Process. (2020). https://doi.org/10.1109/TIP.2020.2990277

    Article  Google Scholar 

  180. Peterson, V., Rufiner, H.L., Spies, R.D.: Kullback-leibler penalized sparse discriminant analysis for event-related potential classification (2016). ar**v preprint ar**v:1608.06863. https://doi.org/10.48550/ar**v.1608.06863

Download references

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Department of Computer Science and Mathematics, School of Science and Technology, The University of Fiji, Lautoka, Fiji

    Neeraj Anand Sharma & A. B. M. Shawkat Ali

  2. School of Computing, Mathematics, and Engineering, Charles Sturt University, Bathurst, NSW, 2795, Australia

    Muhammad Ashad Kabir

Authors
  1. Neeraj Anand Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. B. M. Shawkat Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Muhammad Ashad Kabir
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study’s conception and design. Material preparation, data collection, and analysis were performed by Neeraj Anand Sharma, Professor ABM Shawkat Ali, and Associate Professor Muhammad Ashad Kabir. The first draft of the manuscript was written by Neeraj Anand Sharma and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Neeraj Anand Sharma.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Research involving human and animal participants

This research did not involve any human or animal-based data.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sharma, N.A., Ali, A.B.M.S. & Kabir, M.A. A review of sentiment analysis: tasks, applications, and deep learning techniques. Int J Data Sci Anal (2024). https://doi.org/10.1007/s41060-024-00594-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s41060-024-00594-x

Keywords

Search

Navigation