2021
DOI: 10.24042/tadris.v6i1.8839
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Sentiment Analysis of Religious Moderation in Virtual Public Spaces during the Covid-19 Pandemic

Abstract: In Indonesia, the COVID-19 pandemic has resulted in the virtualization of most social and educational activities, including discourse on the dynamics of Islamic education, which is colored by contextual differences in manhaj, ideology, and religious practice. Social media has evolved into a new role as a multicultural public space that reflects the Indonesian people's religious literacy diversity. As a subsystem of national education, Islamic education plays a critical role in internalizing the Indonesian peop… Show more

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Cited by 3 publications
(3 citation statements)
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“…The integration of technologies such as mobile dev [5] and social media platforms [6] can also generate valuable data for analysis. By blending sentiment analysis, AI, and machine learning with traditional metrics, a more holistic view of online learning environments can be achieved, leading to improved learning experiences [7].…”
Section: Current Research Statusmentioning
confidence: 99%
“…The integration of technologies such as mobile dev [5] and social media platforms [6] can also generate valuable data for analysis. By blending sentiment analysis, AI, and machine learning with traditional metrics, a more holistic view of online learning environments can be achieved, leading to improved learning experiences [7].…”
Section: Current Research Statusmentioning
confidence: 99%
“…Hal lain membuat informan ter-motivasi belajar karena adanya pengaruh dari bentuk religiusitas hal ini sesuai dengan penelitian yang dilakukan (Safrilsyah et al, 2021) dan semangat religius mampu memberikan makna, pencerahan, dan perubahan masa depan peserta didik secara tepat terutama da-lam belajar. Dan dilanjutkan dalam penelitian (Salsabila et al, 2021). me-nyimpulkan bahwa terdapat hubungan positif yang signifikan antara religiusitas dengan perilaku pada peserta didik.…”
Section: Metode Penelitianunclassified
“…Participating in this part, the student's comments were changed and cleaned to be sensibly utilized for dealing with in the accompanying module, the opinion mining module., the attitude mining goes all the way through five natural language processing (NLP) endeavors being in the going with regions. In the main cycle, we use Clancy Database through: Stage 1: Remove copy or immaterial perceptions, Step 2: Fix underlying mistakes, Step 3: Filter undesirable anomalies Stage 4: Handle missing information, Step 5: Validate and quality assurance (QA) (Salsabila, Pratiwi, Ichsan, & Husna, 2021).…”
Section: A Information Preprocessing Componentmentioning
confidence: 99%