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September 27, 2025IETI Transactions on Data Analysis and Forecasting (iTDAF)

Sentiment Analysis and Topic Modelling for Academic Integrity in the Era of AI

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Authors

YMYovie Adhisti MulyonoOKOscar Karnalim

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Overview

Sentiment analysis highlights public views on academic integrity issues in social media, suggesting frameworks for AI challenges.

Key Points

  • Sentiment analysis revealed significant concerns about academic integrity, especially regarding plagiarism.
  • Naive Bayes coupled with Count Vectorizer achieved the best performance in sentiment classification.
  • NMF was the most effective topic modelling technique, generating relevant topics with high coherence scores.
  • Preprocessing steps significantly improved data quality, enhancing both classification and topic modelling outcomes.

Cite This Study

Mulyono et al. (2025) studied this question.

synapsesocial.com/papers/68d7be66eebfec0fc5237e6bhttps://doi.org/10.3991/itdaf.v3i3.56453
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