Comparative study on sentimental analysis using machine learning techniques

  • Murali Krishna Enduri Department of Computer Science and Engineering, SRM University-AP,Amaravati, Guntur India
  • Abdur Rashid Sangi Department of Computer Science, College of Science and Technology, Wenzhou-Kean University, Ouhai, Wenzhou Zhejiang China
  • Satish Anamalamudi Department of Computer Science and Engineering, SRM University-AP,Amaravati, Guntur India
  • Ramanadham Chandu Badrinath Manikanta Department of Computer Science and Engineering, SRM University-AP,Amaravati, Guntur India
  • Kallam Yogeshvar Reddy Department of Computer Science and Engineering, SRM University-AP,Amaravati, Guntur India
  • Panchumarthi Lovely Yeswanth Department of Computer Science and Engineering, SRM University-AP,Amaravati, Guntur India
  • Suda Kiran Sai Reddy Department of Computer Science and Engineering, SRM University-AP,Amaravati, Guntur India
  • Gogineni Asish Karthikeya Department of Computer Science and Engineering, SRM University-AP,Amaravati, Guntur India

Abstract

With the advancement of the Internet and the world wide web (WWW), it is observed that there is an exponential growth of data and information across the internet. In addition, there is a huge growth in digital or textual data generation. This is because users post the reply comments in social media websites based on the experiences about an event or product. Furthermore, people are interested to know whether the majority of potential buyers will have a positive or negative experience on the event or the product. This kind of classification in general can be attained through Sentiment Analysis which inputs unstructured text comments about the product reviews, events, etc., from all the reviews or comments posted by users. This further classifies the data into different categories namely positive, negative or neutral opinions. Sentiment analysis can be performed by different machine learning models like CNN, Naive Bayes, Decision Tree, XgBoost, Logistic Regression etc. The proposed work is compared with the existing solutions in terms of different performance metrics and XgBoost outperforms out of all other methods.

Published
Jan 1, 2023
How to Cite
KRISHNA ENDURI, Murali et al. Comparative study on sentimental analysis using machine learning techniques. Mehran University Research Journal of Engineering and Technology, [S.l.], v. 42, n. 1, p. 207-215, jan. 2023. ISSN 2413-7219. Available at: <https://publications.muet.edu.pk/index.php/muetrj/article/view/2618>. Date accessed: 06 feb. 2023. doi: http://dx.doi.org/10.22581/muet1982.2301.19.
This is an open Access Article published by Mehran University of Engineering and Technolgy, Jamshoro under CCBY 4.0 International License