Abstract : NLP models and sentiment analysis are two core facets of natural language processing (NLP) that are examined in this chapter. It starts by exploring the several approaches used to create NLP models, such as semi-supervised learning, supervised learning, unsupervised learning, and the crucial function of deep learning. It then delves into the field of sentiment analysis, illuminating its importance, variety of approaches, and practical uses. These subjects taken together offer a thorough overview of the rapidly changing field of NLP and its wide variety of applications.
Keywords : Sentiment Analysis, Deep Learning, Supervised Learning, Unsupervised Learning, Semi-Supervised Learning, Natural Language Processing (NLP)
Cite : Gouri, M. H., & Kumar, M. (2023). Sentiment Analysis And Nlp Models As The Language Of Data (1st ed., p. 50). Noble Science Press. https://doi.org/10.52458/9789388996747.nsp2023.eb.ch-07
References :
Schütze, H., Raghavan, P., and Manning, C. D. (2008). Information Retrieval Overview. Cambridge University Press, Cambridge, UK.
Martin, J. H., and D. Jurafsky (2019). Third Edition of Speech and Language Processing. Pearson.
Lee, L., and B. Pang (2008). analysis of sentiment and opinion mining. Information retrieval: Foundations and Trends®, 2(1-2), 1–135.
B. Liu (2012). analysis of sentiment and opinion mining. Human Language Technologies Synthesis Lectures, 5(1), 1-167.
Khan, M. T., Durrani, M., Ali, A., Inayat, I., Khalid, S., & Khan, K. H. (2016). Sentiment analysis and the complex natural language. Complex Adaptive Systems Modeling, 4(1), 1-19.
Wijayanto, A., Bernardo, J. F. A., & Pamungkas, S. (2021). Analisis Klasifikasi Kepuasan Penumpang Maskapai Penerbangan Menggunakan Algoritma Naïve Bayes: Analysis of Airline Passenger Satisfaction Classification Using the Naïve Bayes Algorithm. Jurnal Sains Komputer dan Teknologi Informasi, 3(2), 97-103.
Dongre, M. S. C. S. (2019). E-Mail Spam Classification Using Long Short-Term Memory Method.
