Editor/Reviewer loginFrequency : 12 issues per year
Subject : Computer Applications and Technology
ISSN : 2319–8656 (Online)
IJCATR Volume 13 Issue 10
Comparing Political Inclination Classification on Twitter Posts using Naive Bayes, SVM, and XGBoost
Shashank Shree Neupane, Atish Shakya, Bishan Rokka, Sagar Acharya
10.7753/IJCATR1310.1005
keywords : Political inclinations, Twitter data analysis, Machine Learning, Natural Language Processing, Data Preprocessing
For centuries, ideology has been reflected in a person’s expression. The expression points out the bias or support the person holds. Nowadays, expressions are well seen on social media in the form of text. X (Formerly Twitter) has become the favoured medium for these expressions. Nepal, a politically highly influenced country where political changes have been frequent in a short period, has people’s thoughts expressed on social media. This paper presents a novel approach to finding a person’s political inclination through their Nepali tweet using machine learning techniques. By leveraging data pre-processing and XGBoost, we achieve a promising accuracy of 73%. We also discuss potential avenues for further improving accuracy, such as expanding the dataset to include other social media platforms and enhancing data pre-processing techniques.
@artical{s13102024ijcatr13101005,
Title = "Comparing Political Inclination Classification on Twitter Posts using Naive Bayes, SVM, and XGBoost",
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "13",
Issue ="10",
Pages ="62 - 65",
Year = "2024",
Authors ="Shashank Shree Neupane, Atish Shakya, Bishan Rokka, Sagar Acharya"}
The Twitter Dataset is Made Public for future Researchers.
The paper does model training in the Nepali dataset for the Natural Language Processing.
The proven models are compared to get the best evaluations.
The models are gone through rigorous hyper parameter tuning to get best result.