Open Access

ARTICLE

Sentiment Analysis with Tweets Behaviour in Twitter Streaming API

by Kuldeep Chouhan¹, Mukesh Yadav², Ranjeet Kumar Rout³, Kshira Sagar Sahoo⁴, NZ Jhanjhi^5,*, Mehedi Masud⁶, Sultan Aljahdali⁶

1 Computer Science and Engineering, I. T. S Engineering College, Greater Noida, 201310, India
2 Computer Science and Engineering, DPG Institute of Technology and Management, Gurgaon, 122004, India
3 Computer Science and Engineering, National Institute of Technology Srinagar, Jammu and Kashmir, India
4 Department of Computer Science and Engineering, SRM University, Amaravati, Andhra Pradesh, 522240, India
5 School of Computer Science SCS, Taylor’s University, Subang Jaya, 47500, Malaysia
6 Department of Computer Science, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif, 21944, Saudi Arabia

* Corresponding Author: NZ Jhanjhi. Email:

Computer Systems Science and Engineering 2023, 45(2), 1113-1128. https://doi.org/10.32604/csse.2023.030842

Received 03 April 2022; Accepted 08 June 2022; Issue published 03 November 2022

Abstract

Twitter is a radiant platform with a quick and effective technique to analyze users’ perceptions of activities on social media. Many researchers and industry experts show their attention to Twitter sentiment analysis to recognize the stakeholder group. The sentiment analysis needs an advanced level of approaches including adoption to encompass data sentiment analysis and various machine learning tools. An assessment of sentiment analysis in multiple fields that affect their elevations among the people in real-time by using Naive Bayes and Support Vector Machine (SVM). This paper focused on analysing the distinguished sentiment techniques in tweets behaviour datasets for various spheres such as healthcare, behaviour estimation, etc. In addition, the results in this work explore and validate the statistical machine learning classifiers that provide the accuracy percentages attained in terms of positive, negative and neutral tweets. In this work, we obligated Twitter Application Programming Interface (API) account and programmed in python for sentiment analysis approach for the computational measure of user’s perceptions that extract a massive number of tweets and provide market value to the Twitter account proprietor. To distinguish the results in terms of the performance evaluation, an error analysis investigates the features of various stakeholders comprising social media analytics researchers, Natural Language Processing (NLP) developers, engineering managers and experts involved to have a decision-making approach.

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Keywords

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