التفاصيل البيبلوغرافية
| العنوان: |
Exploring Sentiments in the Russia-Ukraine Conflict: A Comparative Analysis of KNN, Decision Tree And Logistic Regression Machine Learning Classifiers. |
| المؤلفون: |
Sinha, Aaryan, Rout, Bijayalaxmi, Mohanty, Sushree, Mishra, Soumya Ranjan, Mohapatra, Hitesh, Dey, Samik |
| المصدر: |
Procedia Computer Science; 2024, Vol. 235, p1068-1076, 9p |
| مصطلحات موضوعية: |
RUSSIA-Ukraine Conflict, 2014-, RUSSIAN invasion of Ukraine, 2022-, NATURAL language processing, DECISION trees, LOGISTIC regression analysis, K-nearest neighbor classification, SOCIAL media, VIRTUAL communities |
| مصطلحات جغرافية: |
UKRAINE, RUSSIA |
| الشركة/الكيان: |
X Corp. |
| مستخلص: |
The outbreak of conflict between Russia and Ukraine on February 23, 2022, was a huge shock, and it quickly became the dominant topic of conversation on social media. Our research is based on Twitter conversations about the conflict between Russia and Ukraine. This paper intends to monitor and analyze the Twitter conversation on the continuing war in Ukraine using natural language processing techniques such as word cloud analysis, sentiment analysis, and visualization. The major goal is to acquire insight into Twitter users' shifting thoughts and opinions on various parts of the conflict over time. The study's findings have important implications for politicians, journalists, and social media users who want to follow and understand how online communities' attitudes on global issues change. We hope that this study will help to gain a deeper knowledge of how social media works in affecting public debate and how it might be used to gain insights into public opinion on crucial global issues. In this work logistic regression, K-nearest neighbours and decision tree algorithm are used to analyse the sentiments and got classification accuracy of 94.58%, 88.89% and 90.45% respectively. [ABSTRACT FROM AUTHOR] |
|
Copyright of Procedia Computer Science is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Supplemental Index |
Full Text Finder