A Review on Sentiment and Emotion Analysis for Computational Literary Studies

Nasrullah Makhdom; H N Verma; Arun Kumar Yadav

doi:10.32628/CSEIT241029

Authors

Nasrullah Makhdom M.Tech. Student, Department of CSE, ITM University Gwalior, Madhya Pradesh, India Author
H N Verma Associate Professor, Department of CSE, ITM University Gwalior, Madhya Pradesh, India Author
Arun Kumar Yadav Associate Professor, Department of CSE, ITM University Gwalior, Madhya Pradesh, India Author

DOI:

https://doi.org/10.32628/CSEIT241029

Keywords:

Sentiment Analysis, Emotion Analysis, Digital Humanities, Computational Literature, Opinion Mining

Abstract

In sentiment analysis, emotions refer to the subjective feelings expressed in a text or speech that can be classified as positive, negative or neutral. Emotions are an important aspect of sentiment analysis because they provide insights into the attitudes, opinions and behaviors of individuals toward a particular topic or entity. The emergence of digital humanities has allowed for a more computational approach to understanding emotions in literature. The passage provides an overview of existing research in this area and understanding the emotionality involved in text. Throughout this survey, it has been demonstrated that sentiment and emotion analysis is increasingly attracting attention within the field of digital humanities, particularly in computational literary studies.

Downloads

Download data is not yet available.

References

Kim, E., & Klinger, R. (2018). A survey on sentiment and emotion analysis for computational literary studies. arXiv preprint arXiv:1808.03137.

Ortony, A., Clore, G. L., & Collins, A. (2022). The cognitive structure of emotions. Cambridge university press. DOI: https://doi.org/10.1017/9781108934053

Darwin, C., & Prodger, P. (1998). The expression of the emotions in man and animals. Oxford University Press, USA. DOI: https://doi.org/10.1093/oso/9780195112719.002.0002

Blackwood, S. (2010). Isabel archer's body. The Henry James Review, 31(3), pp 271-279. DOI: https://doi.org/10.1353/hjr.2010.a402526

Dixon, T. (2012). “Emotion”: The history of a keyword in crisis. Emotion Review, 4(4), pp 338-344. DOI: https://doi.org/10.1177/1754073912445814

Solomon, R. C. (2008). True to our feelings: What our emotions are really telling us. Oxford University Press. DOI: https://doi.org/10.1093/acprof:oso/9780195368536.001.0001

Soleymani, M., Garcia, D., Jou, B., Schuller, B., Chang, S. F., & Pantic, M. (2017). A survey of multimodal sentiment analysis. Image and Vision Computing, 65, pp 3-14. DOI: https://doi.org/10.1016/j.imavis.2017.08.003

Vora, M., Blau, T., Kachhwal, V., Solo, A. M., & Chandra, R. (2024). Large language model for Bible sentiment analysis: Sermon on the Mount. arXiv preprint arXiv:2401.00689.

Srisankar, M. (2024). A Survey on Sentiment Analysis Techniques in the Medical Domain. Medicon Agriculture & Environmental Sciences, 6, pp 04-09.

Heidari, M., & Rafatirad, S. (2020, October). Using transfer learning approach to implement convolutional neural network model to recommend airline tickets by using online reviews. In 2020 15th International Workshop on Semantic and Social Media Adaptation and Personalization, SMA, pp 1-6. IEEE. DOI: https://doi.org/10.1109/SMAP49528.2020.9248443

Heidari, M., & Jones, J. H. (2020, October). Using bert to extract topic-independent sentiment features for social media bot detection. In 2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference, UEMCON, pp 0542-0547, IEEE. DOI: https://doi.org/10.1109/UEMCON51285.2020.9298158

Chandra, R., & Kulkarni, V. (2022). Semantic and sentiment analysis of selected Bhagavad Gita translations using BERT-based language framework. IEEE Access, 10, pp 21291-21315. DOI: https://doi.org/10.1109/ACCESS.2022.3152266

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

Kirill, Y., Mihail, I. G., Sanzhar, M., Rustam, M., Olga, F., & Ravil, M. (2020). Propaganda identification using topic modelling. Procedia Computer Science, 178, pp 205-212. DOI: https://doi.org/10.1016/j.procs.2020.11.022

Egger, R. (2022). Topic Modelling: Modelling Hidden Semantic Structures in Textual Data. In Applied Data Science in Tourism: Interdisciplinary Approaches, Methodologies and Applications, pp 375-403. Cham: Springer International Publishing. DOI: https://doi.org/10.1007/978-3-030-88389-8_18

Bertoldi, N., Zens, R., & Federico, M. (2007, April). Speech translation by confusion network decoding. In 2007 IEEE International Conference on Acoustics, Speech and Signal Processing-ICASSP'07, Vol. 4, pp IV-1297, IEEE. DOI: https://doi.org/10.1109/ICASSP.2007.367315

Nakamura, S., Markov, K., Nakaiwa, H., Kikui, G. I., Kawai, H., Jitsuhiro, T., & Yamamoto, S. (2006). The ATR multilingual speech-to-speech translation system. IEEE Transactions on Audio, Speech and Language Processing, 14(2), pp 365-376. DOI: https://doi.org/10.1109/TSA.2005.860774

Mikheev, A., Moens, M., & Grover, C. (1999, June). Named entity recognition without gazetteers. In Ninth Conference of the European Chapter of the Association for Computational Linguistics, pp 1-8. DOI: https://doi.org/10.3115/977035.977037

Marrero, M., Urbano, J., Sánchez-Cuadrado, S., Morato, J., & Gómez-Berbís, J. M. (2013). Named entity recognition: fallacies, challenges and opportunities. Computer Standards & Interfaces, 35(5), pp 482-489. DOI: https://doi.org/10.1016/j.csi.2012.09.004

Deonna, J., & Teroni, F. (2012). The emotions: A philosophical introduction. Routledge. DOI: https://doi.org/10.4324/9780203721742

Munezero, M., Montero, C. S., Sutinen, E., & Pajunen, J. (2014). Are they different? Affect, feeling, emotion, sentiment and opinion detection in text. IEEE transactions on affective computing, 5(2), pp 101-111. DOI: https://doi.org/10.1109/TAFFC.2014.2317187

Hung, L. P., & Alias, S. (2023). Beyond sentiment analysis: A review of recent trends in text-based sentiment analysis and emotion detection. Journal of Advanced Computational Intelligence and Intelligent Informatics, 27(1), pp 84-95. DOI: https://doi.org/10.20965/jaciii.2023.p0084

Chauhan, D. S., Akhtar, M. S., Ekbal, A., & Bhattacharyya, P. (2019, November). Context-aware interactive attention for multi-modal sentiment and emotion analysis. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP, pp 5647-5657. DOI: https://doi.org/10.18653/v1/D19-1566

Zad, S., Heidari, M., James Jr, H., & Uzuner, O. (2021, May). Emotion detection of textual data: An interdisciplinary survey. In 2021 IEEE World AI IoT Congress, AIIoT, pp 0255-0261, IEEE. DOI: https://doi.org/10.1109/AIIoT52608.2021.9454192

Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. . Foundations and Trends® in information retrieval, 2(1–2), 1-135. DOI: https://doi.org/10.1561/1500000011

James, W. (1922). The emotions.

Iser, W. (1975). The reality of fiction: a functionalist approach to literature. New Literary History, 7(1), pp 7-38. DOI: https://doi.org/10.2307/468276

Carbonell, J. G. (1979). Subjective understanding: computer models of belief systems. Yale University.

Wilks, Y., & Bien, J. (1983). Beliefs, points of view and multiple environments. Cognitive Science, 7(2), pp 95-119. DOI: https://doi.org/10.1016/S0364-0213(83)80007-X

Mohammad, S. M. (2016). Sentiment analysis: Detecting valence, emotions and other affectual states from text. In Emotion measurement, pp 201-237. Woodhead Publishing. DOI: https://doi.org/10.1016/B978-0-08-100508-8.00009-6

Wiebe, J., Wilson, T., & Cardie, C. (2004). Annotating expressions of opinions and emotions in. To appear in Language Resources and Evaluation, 1, 2. DOI: https://doi.org/10.1007/s10579-005-7880-9

Hutto, C., & Gilbert, E. (2014, May). Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the international AAAI conference on web and social media, Vol. 8, No. 1, pp 216-225. DOI: https://doi.org/10.1609/icwsm.v8i1.14550

Mishne, G. (2005, August). Experiments with mood classification in blog posts. In Proceedings of ACM SIGIR 2005 workshop on stylistic analysis of text for information access , Vol. 19, pp 321-327.

Alm, C. O., Roth, D., & Sproat, R. (2005, October). Emotions from text: machine learning for text-based emotion prediction. In Proceedings of human language technology conference and conference on empirical methods in natural language processing, pp 579-586. DOI: https://doi.org/10.3115/1220575.1220648

Hovy, D., & Lavid, J. (2010). Towards genre-based sentiment analysis. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics(ACL), pp 395-403.