Comparison of Public and Critics Opinion About the Taliban Government Over Afghanistan Through Sentiment Analysis

dc.contributor.authorReza, Md Majid
dc.contributor.authorSingh, Satwinder
dc.contributor.authorKundra, Harish
dc.contributor.authorReza, Md Rashid
dc.date.accessioned2024-01-21T10:48:41Z
dc.date.accessioned2024-08-14T05:05:35Z
dc.date.available2024-01-21T10:48:41Z
dc.date.available2024-08-14T05:05:35Z
dc.date.issued2023-05-03T00:00:00
dc.description.abstractThe usage of social media has increased exponentially these days. People worldwide are sharing their opinions on different platforms such as Twitter, personal blogs, Facebook, and other similar platforms. Twitter has grown in popularity as a platform for people to express their thoughts and opinions on many different topics. The data from Twitter about the Taliban has been examined in this research work, and various machine learning algorithms have been applied including SVM, LR, and random forest. Text sentiments have been captured via TextBlob. Among the machine learning models applied, SVM outperformed all other models and achieved an accuracy score of around 94% on the tweet dataset and logistic regression outperformed other models with an accuracy score of 83% on the news article dataset. � 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.en_US
dc.identifier.doi10.1007/978-981-19-7455-7_33
dc.identifier.isbn9789811974540
dc.identifier.issn23673370
dc.identifier.urihttps://kr.cup.edu.in/handle/32116/3922
dc.identifier.urlhttps://link.springer.com/10.1007/978-981-19-7455-7_33
dc.language.isoen_USen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.subjectHashtagsen_US
dc.subjectMachine learningen_US
dc.subjectNLPen_US
dc.subjectSentiment analysisen_US
dc.subjectTextBloben_US
dc.subjectTweetsen_US
dc.titleComparison of Public and Critics Opinion About the Taliban Government Over Afghanistan Through Sentiment Analysisen_US
dc.title.journalLecture Notes in Networks and Systemsen_US
dc.typeConference paperen_US
dc.type.accesstypeClosed Accessen_US

Files