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Sentiment Analysis of Korean Modern Novel Texts Applying Deep Learning Models

  
26 mar 2025
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Mounika, A., & Saraswathi, S. (2021). Sentiment analysis of book reviews using CNN with n-grams method. International Journal of Knowledge Engineering and Data Mining, 7(1-2), 64-85. MounikaA. & SaraswathiS. (2021). Sentiment analysis of book reviews using CNN with n-grams method. International Journal of Knowledge Engineering and Data Mining, 7(1-2), 64-85.Search in Google Scholar

Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review, 55(7), 5731-5780. WankhadeM.RaoA. C. S. & KulkarniC. (2022). A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review, 55(7), 5731-5780.Search in Google Scholar

Lamba, M., & Madhusudhan, M. (2021). Sentiment analysis. In Text mining for information professionals: An uncharted territory (pp. 191-211). Cham: Springer International Publishing. LambaM. & MadhusudhanM. (2021). Sentiment analysis. In Text mining for information professionals: An uncharted territory (pp. 191-211). Cham: Springer International Publishing.Search in Google Scholar

Bharathi, R., Bhavani, R., & Priya, R. (2024). Leveraging deep learning with sentiment analysis for Online Book reviews polarity classification model. Multimedia Tools and Applications, 1-20. BharathiR.BhavaniR. & PriyaR. (2024). Leveraging deep learning with sentiment analysis for Online Book reviews polarity classification model. Multimedia Tools and Applications, 1-20.Search in Google Scholar

Hussein, D. M. E. D. M. (2018). A survey on sentiment analysis challenges. Journal of King Saud University-Engineering Sciences, 30(4), 330-338. HusseinD. M. E. D. M. (2018). A survey on sentiment analysis challenges. Journal of King Saud University-Engineering Sciences, 30(4), 330-338.Search in Google Scholar

Gogula, S. D., Rahouti, M., Gogula, S. K., Jalamuri, A., & Jagatheesaperumal, S. K. (2023). An emotion-based rating system for books using sentiment analysis and machine learning in the cloud. Applied Sciences, 13(2), 773. GogulaS. D.RahoutiM.GogulaS. K.JalamuriA. & JagatheesaperumalS. K. (2023). An emotion-based rating system for books using sentiment analysis and machine learning in the cloud. Applied Sciences, 13(2), 773.Search in Google Scholar

Mounika, A., & Saraswathi, S. (2021). Design of book recommendation system using sentiment analysis. In Evolutionary Computing and Mobile Sustainable Networks: Proceedings of ICECMSN 2020 (pp. 95-101). Springer Singapore. MounikaA. & SaraswathiS. (2021). Design of book recommendation system using sentiment analysis. In Evolutionary Computing and Mobile Sustainable Networks: Proceedings of ICECMSN 2020 (pp. 95-101). Springer Singapore.Search in Google Scholar

Devika, P., & Milton, A. (2024). Book recommendation using sentiment analysis and ensembling hybrid deep learning models. Knowledge and Information Systems, 1-38. DevikaP. & MiltonA. (2024). Book recommendation using sentiment analysis and ensembling hybrid deep learning models. Knowledge and Information Systems, 1-38.Search in Google Scholar

Srujan, K. S., Nikhil, S. S., Raghav Rao, H., Karthik, K., Harish, B. S., & Keerthi Kumar, H. M. (2018). Classification of Amazon book reviews based on sentiment analysis. In Information Systems Design and Intelligent Applications: Proceedings of Fourth International Conference INDIA 2017 (pp. 401-411). Springer Singapore. SrujanK. S.NikhilS. S.Raghav RaoH.KarthikK.HarishB. S. & Keerthi KumarH. M. (2018). Classification of Amazon book reviews based on sentiment analysis. In Information Systems Design and Intelligent Applications: Proceedings of Fourth International Conference INDIA 2017 (pp. 401-411). Springer Singapore.Search in Google Scholar

Min, S., & Park, J. (2019). Modeling narrative structure and dynamics with networks, sentiment analysis, and topic modeling. PloS one, 14(12), e0226025. MinS. & ParkJ. (2019). Modeling narrative structure and dynamics with networks, sentiment analysis, and topic modeling. PloS one, 14(12), e0226025.Search in Google Scholar

Bizzoni, Y., Moreira, P., Thomsen, M. R., & Nielbo, K. (2023, July). Sentimental matters-predicting literary quality by sentiment analysis and stylometric features. In Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis (pp. 11-18). BizzoniY.MoreiraP.ThomsenM. R. & NielboK. (2023, July). Sentimental matters-predicting literary quality by sentiment analysis and stylometric features. In Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis (pp. 11-18).Search in Google Scholar

Levitt, P., & Shim, B. S. (2022). Producing Korean literature (KLit) for export. The Journal of Chinese Sociology, 9(1), 10. LevittP. & ShimB. S. (2022). Producing Korean literature (KLit) for export. The Journal of Chinese Sociology, 9(1), 10.Search in Google Scholar

Taehoon, R., Bruce, J. C., & Fulton, J. C. (2021). Genre Fiction in Korean Literature. Azalea: Journal of Korean Literature & Culture, 14(14), 13-23. TaehoonR.BruceJ. C. & FultonJ. C. (2021). Genre Fiction in Korean Literature. Azalea: Journal of Korean Literature & Culture, 14(14), 13-23.Search in Google Scholar

Yoo, J. J. (2017). Networks of disquiet: censorship and the production of literature in eighteenth-century Korea. Acta Koreana, 20(1), 249-280. YooJ. J. (2017). Networks of disquiet: censorship and the production of literature in eighteenth-century Korea. Acta Koreana, 20(1), 249-280.Search in Google Scholar

Medina, J. W. (2018). At the Gates of Babel: The Globalization of Korean Literature as World Literature. Acta Koreana, 21(2), 395-421. MedinaJ. W. (2018). At the Gates of Babel: The Globalization of Korean Literature as World Literature. Acta Koreana, 21(2), 395-421.Search in Google Scholar

Lee, J. (2018). IM HWA, HYBRIDITY, AND THE ANTI-COLONIAL POLITICS OF MODERN KOREAN LITERATURE. Kritika Kultura. LeeJ. (2018). IM HWA, HYBRIDITY, AND THE ANTI-COLONIAL POLITICS OF MODERN KOREAN LITERATURE. Kritika Kultura.Search in Google Scholar

JO, Y. J. (2024). (Un) visualizing Korea in the World: The Issue of the Translator in the Collection of Modern Korean Fairy Tales and the Politics of the World Fairy Tale Series. Korea Journal, 64(3). JOY. J. (2024). (Un) visualizing Korea in the World: The Issue of the Translator in the Collection of Modern Korean Fairy Tales and the Politics of the World Fairy Tale Series. Korea Journal, 64(3).Search in Google Scholar

Sang-Bin, L. E. E. (2019). Marshall R. Pihl and His Views on How to Enrich Korean Literature in Translation. Sungkyun Journal of East Asian Studies, 19(2), 147-165. Sang-BinL. E. E. (2019). Marshall R. Pihl and His Views on How to Enrich Korean Literature in Translation. Sungkyun Journal of East Asian Studies, 19(2), 147-165.Search in Google Scholar

Kumar, S., Gahalawat, M., Roy, P. P., Dogra, D. P., & Kim, B. G. (2020). Exploring impact of age and gender on sentiment analysis using machine learning. Electronics, 9(2), 374. KumarS.GahalawatM.RoyP. P.DograD. P. & KimB. G. (2020). Exploring impact of age and gender on sentiment analysis using machine learning. Electronics, 9(2), 374.Search in Google Scholar

Suhendra, N. H. B., Keikhosrokiani, P., Asl, M. P., & Zhao, X. (2022). Opinion mining and text analytics of literary reader responses: A case study of reader responses to KL Noir volumes in Goodreads using sentiment analysis and topic. In Handbook of research on opinion mining and text analytics on literary works and social media (pp. 191-239). IGI Global. SuhendraN. H. B.KeikhosrokianiP.AslM. P. & ZhaoX. (2022). Opinion mining and text analytics of literary reader responses: A case study of reader responses to KL Noir volumes in Goodreads using sentiment analysis and topic. In Handbook of research on opinion mining and text analytics on literary works and social media (pp. 191-239). IGI Global.Search in Google Scholar

Jürgen Dietrich & André Hollstein. (2024). Performance and Reproducibility of Large Language Models in Named Entity Recognition: Considerations for the Use in Controlled Environments. . Drug safety(prepublish),1-17. DietrichJürgen & HollsteinAndré. (2024). Performance and Reproducibility of Large Language Models in Named Entity Recognition: Considerations for the Use in Controlled Environments. . Drug safety(prepublish),1-17.Search in Google Scholar

Yedida Venkata Rama Subramanaya Viswanadham & K. Annapurani Panaiyappan. (2024). Adaptive Deep Conditional Random Field-Based Blockchain Access with Hybrid Encryption for Data Privacy Preservation. SN Computer Science(8),1087-1087. Rama Subramanaya ViswanadhamYedida Venkata & PanaiyappanK. Annapurani. (2024). Adaptive Deep Conditional Random Field-Based Blockchain Access with Hybrid Encryption for Data Privacy Preservation. SN Computer Science(8),1087-1087.Search in Google Scholar

Jing Zhou,Zhanliang Ye,Sheng Zhang,Zhao Geng,Ning Han & Tao Yang. (2024). Investigating response behavior through TF-IDF and Word2vec text analysis: A case study of PISA 2012 problem-solving process data. Heliyon(16),e35945-e35945. ZhouJingYeZhanliangZhangShengGengZhaoHanNing & YangTao. (2024). Investigating response behavior through TF-IDF and Word2vec text analysis: A case study of PISA 2012 problem-solving process data. Heliyon(16),e35945-e35945.Search in Google Scholar

Lingua:
Inglese
Frequenza di pubblicazione:
1 volte all'anno
Argomenti della rivista:
Scienze biologiche, Scienze della vita, altro, Matematica, Matematica applicata, Matematica generale, Fisica, Fisica, altro