A Study on Sentimental Analysis using Machine Learning Approach

P Praveen Kumar; PS Gurunadh; Balajee Maram

P Praveen Kumar Student, GMR Institute Of Technology.
PS Gurunadh Student, Department of Computer Science, GMR Institute of Technology, Rajam, Andhra Pradesh, India
Balajee Maram Associate Professor, Department of Computer Science, GMR Institute of Technology, Rajam, Andhra Pradesh, India.

Abstract

Nowadays, people using online platforms for exchanging ideas, sharing their views and learning through online platforms. Social media is filling up with huge data which are the various tweets, various blogs, and updates on posts and products etc. That data is unstructured and it needs to be organized and analyzed. Companies and many other people are trying to work on that data for long time to improve their position in the market. Sentimental Analysis plays a crucial role in this data classification. Sentimental analysis is a Natural Language Processing technique. This analysis classifies reviews data and opinions into positive, neutral and negative. This analysis can be done using machine learning. Machine learning is making the machines to learn from their past experiences. Machine learning importance is getting increased exponentially. This study aims to learn different levels of sentiments and steps that are involved in sentimental analysis and some machine learning algorithms for sentiment classification.

How to cite this article:
Kumar PP, Gurunandh PS, Maram B. A Study on Sentimental Analysis using Machine Learning Approach. J Adv Res Appl Arti Intel Neural Netw 2021; 5(1): 1-4.

References

[1] Singh, N. K., Tomar, D. S., & Sangaiah, A. K., “Sentiment analysis: a review and comparative analysis over social media”, Journal of Ambient Intelligence and Humanized Computing, Volume 11, No.1, 2020, pp. 97-117.
[2] Jagdale, Rajkumar S., Vishal S. Shirsat, and Sachin N. Deshmukh. "Sentiment analysis on product reviews using machine learning techniques." In Cognitive Informatics and Soft Computing, pp. 639-647. Springer, Singapore, 2019.
[3] Krishna, A., Akhilesh, V., Aich, A., & Hegde, C. “Sentiment Analysis of Restaurant Reviews Using Machine Learning Techniques”, In Emerging Research in Electronics, Computer Science and Technology, Springer, Singapore, 2019, pp. 687-696.
[4] Singh, J., Singh, G., & Singh, R. “Optimization of sentiment analysis using machine learning classifiers”, Human-centric Computing and information Sciences, Volume 7, No 1, 32 ,2017.
[5] Kumar, S., Gahalawat, M., Roy, P. P., Dogra, D. P., & Kim, B. G. Exploring Impact of Age and Gender on Sentiment Analysis Using Machine Learning. Electronics, Volume 9, No 2, 374, 2020.
[6] Le, B., & Nguyen, H. “Twitter sentiment analysis using machine learning techniques”, In Advanced Computational Methods for Knowledge Engineering, 2015, pp. 279-289. Springer, Cham.
[7] Patil, P., & Yalagi, P. “Sentimental Analysis Levels and Techniques: A Survey”. vol, 5, 2016, PP. 3030-3035.