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The extensive LDA experiments highlighted key themes such as "Academic Difficulties," "Financial Difficulties," "Pandemic-Related Challenges," "Grant Disbursement," and "Program Implementation." The analysis underscores the significance of these core topics, illustrating the model's ability to identify and prioritize crucial themes relevant to the dataset. Integrating Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) methods with LDA remarkably enhances the model's interpretability. BoW contributed to improved topic identification and model stability, ensuring clear and consistent topic representation. On the other hand, TF-IDF refined topic descriptors and balanced word representation, offering nuanced and contextually relevant topics by focusing on unique words. |
IEEE/ICACT20240447 Slide.17
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