Sentiment analysis of Twitter data using machine learning: COVID-19 perspective
by Shobhit Srivastava; Mrinal Kanti Sarkar; Chinmay Chakraborty
International Journal of Data Analysis Techniques and Strategies (IJDATS), Vol. 16, No. 1, 2024

Abstract: The 2019 COVID-19 pandemic has affected people worldwide. Social media has become a global platform for individuals to voice their diverse perspectives on the pandemic, which has significantly altered their lives during and beyond lockdown periods. Twitter, a leading social media platform, experienced a surge in coronavirus-related tweets encompassing a spectrum of positive, negative and neutral opinions. Coronavirus transmits between humans in numerous ways. It irritates the lungs. This makes Twitter a perfect platform for expressing opinions. Twitter data from across the world was collected and analysed for sentiment in order to better understand public opinion and prepare for COVID-19 (Tusar et al., 2022). In this article, our aim is to compare the neural network techniques and indicate the share of their performance measures. We use kNN and neural network algorithms for these and use the MSE factor as a key of comparison. However, we use other performance measures too for better analysis of the result. Our main focus in this study is to analyse the performance partition of the kNN algorithms, including the performance portion of the each algorithm.

Online publication date: Tue, 19-Mar-2024

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Analysis Techniques and Strategies (IJDATS):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com