Study of Machine Learning and Deep Learning Model for Sentiment Analysis
Keywords:
Machine Learning, Deep Learning, Sentiment AnalysisAbstract
Sentiment analysis is one of the prevalent and hotspot research areas. Sentiment analysis research has expanded laterally and has its applications in many other research areas. As the applications of sentiment analysis grew, one of the most fine-grained form in demand is Aspect level sentiment analysis, the field which identifies the product features/aspects that are talked about in the review and hence map a sentiment to each aspect. There has been active research in the field of sentiment analysis for a couple of decades where the task of aspect term extraction which identifies the product features/aspects in the review has been found more challenging. The large volume of unlabeled data definitely encourages the need for more unsupervised models to be experimented in the field. Supervised models are definitely outperforming unsupervised models but are constrained by the availability of labelled data for the fine-grained task. There is a huge cost, time and effort involved in creating voluminous and qualitative labelled datasets for training data required by supervised models.
References
Cach N. Dang, María N. Moreno-García and Fernando De la Prieta, “Hybrid Deep Learning Models for Sentiment Analysis”, Hindawi, 2021.
Q. Zhang, Z. Zhang, M. Yang, and L. Zhu, “Exploring coevolution of emotional contagion and behavior for microblog sentiment analysis: a deep learning architecture,” Complexity, vol. 2021, Article ID 6630811, 10 pages, 2021.
H. Kaur, S. U. Ahsaan, B. Alankar, and V. Chang, “A proposed sentiment analysis deep learning algorithm for analyzing COVID-19 tweets,” Information Systems Frontiers, pp. 1–13, 2021.
N. C. Dang, M. N. Moreno-García, and F. De la Prieta, “Sentiment analysis based on deep learning: a comparative study,” Electronics, vol. 9, no. 3, p. 483, 2020.
G. Wang, G. Yu, and X. Shen, “The effect of online investor sentiment on stock movements: an LSTM approach,” Complexity, vol. 2020, Article ID 4754025, 11 pages, 2020.
A. Rajput, “Natural language processing, sentiment analysis, and clinical analytics,” in Innovation in Health Informatics, pp. 79–97, Academic Press, Cambridge, MA, USA, 2020.
A. S. M. Alharbi and E. de Doncker, “Twitter sentiment analysis with a deep neural network: an enhanced approach using user behavioral information,” Cognitive Systems Research, vol. 54, pp. 50–61, 2019.
F. Abid, M. Alam, M. Yasir, and C. Li, “Sentiment analysis through recurrent variants latterly on convolutional neural network of Twitter,” Future Generation Computer Systems, vol. 95, pp. 292–308, 2019.
N. Jnoub, F. Al Machot, and W. Klas, “A domain-independent classification model for sentiment analysis using neural models,” Applied Sciences, vol. 10, no. 18, p. 6221, 2020.
J. F. Sanchez-Rada and C. A. Iglesias, “CRANK: a hybrid model for user and content sentiment classification using social context and community detection,” Applied Sciences, vol. 10, no. 5, p. 1662, 2020.
R. Alfrjani, T. Osman, and G. Cosma, “A hybrid semantic knowledgebase-machine learning approach for opinion mining,” Data & Knowledge Engineering, vol. 121, pp. 88–108, 2019.
M. J. S. Keenan, Advanced Positioning, Flow, and Sentiment Analysis in Commodity Markets: Bridging Fundamental and Technical Analysis, Wiley, Hoboken, NJ, USA, 2nd edition, 2018.
S. Sohangir, D. Wang, A. Pomeranets, and T. M. Khoshgoftaar, “Big data: deep learning for financial sentiment analysis,” Journal of Big Data, vol. 5, no. 1, p. 3, 2018.
H. Jangid, S. Singhal, R. R. Shah, and R. Zimmermann, “Aspect-based financial sentiment analysis using deep learning,” in Companion Proceedings of the Web Conference 2018, International World Wide Web Conferences Steering Committee, Lyon, France, April 2018.
V. Malik and A. Kumar, “Sentiment analysis of twitter data using Naive Bayes algorithm,” International Journal on Recent and Innovation Trends in Computing and Communication, vol. 6, no. 4, pp. 120–125, 2018.
P. Vateekul and T. Koomsubha, “A study of sentiment analysis using deep learning techniques on Thai Twitter data,” in 2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE), IEEE, Khon Kaen, Thailand, July 2016.
A. C. Pandey, D. S. Rajpoot, and M. Saraswat, “Twitter sentiment analysis using hybrid cuckoo search method,” Information Processing & Management, vol. 53, no. 4, pp. 764–779, 2017.
Downloads
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.