Identification of Islamophobia Sentiment Analysis on Twitter Using Text Mining Language Detection

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.