Identification of islamophobia sentiment analysis on Twitter using text mining language detection

Kurniawan, Fachrul ORCID: https://orcid.org/0000-0002-3709-8764, Badruddin, Badruddin and Wibawa, Prasetya Aji (2022) Identification of islamophobia sentiment analysis on Twitter using text mining language detection. Journal of Positive School Psychology, 6 (5). pp. 8286-8294. ISSN 2717-7564

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Abstract

This research aims to discover the issue of sentiment analysis related to Islamophobia in social media, especially Twitter. The text mining and language detection approach was used to perform the data collection and selection. By identifying a text's polarity, sentiment analysis is a technique for extracting information from a person's attitude about an issue or occurrence. The grouping is made to discuss whether the reader is positive or negative. Moreover, the machine learning approaches also performed using long-short term memory (LSTM) and support vector machine (SVM) to classify the data. From the pre-processing stage, the drop duplication procedure creates 4339 from the preceding 10997, and the result language detection is 31 languages. Although the data comes from the world's largest Muslim country, the problem is not limited to it, as evidenced by using text mining tools to identify languages.

Item Type:	Journal Article
Keywords:	social media; Islamophobia; language; text mining
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080107 Natural Language Processing
Divisions:	Faculty of Technology > Department of Informatics Engineering
Depositing User:	Fachrul Kurniawan
Date Deposited:	03 Aug 2022 15:20

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