Summary of Quantifying the Effectiveness Of Student Organization Activities Using Natural Language Processing, by Lyberius Ennio F. Taruc et al.
Quantifying the Effectiveness of Student Organization Activities using Natural Language Processing
by Lyberius Ennio F. Taruc, Arvin R. De La Cruz
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel machine learning workflow is developed to quantify the effectiveness of student-organized activities based on student emotional responses using sentiment analysis. The workflow utilizes the BERT Large Language Model (LLM) and a Transformer pipeline in Hugging Face, leveraging pysentimiento toolkit. A sample dataset from College X’s Recognized Student Organization (RSO) was used to develop the workflow. Data preprocessing, key feature selection, LLM feature processing, and score aggregation were performed, resulting in an Event Score for each dataset. The BERT LLM demonstrates its effectiveness in analyzing sentiment beyond product reviews and post comments. This study showcases the potential impact of data-driven decision making in student affairs offices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Student-organized activities are a big part of what makes school special. Researchers want to find new ways to make these activities better using machines that can understand language, like computers can play chess really well. They created a special tool that uses a powerful computer program called BERT to figure out how students feel about different events. They tested it with data from a college in the Philippines and found that it worked pretty well! This could help colleges make decisions based on student feelings instead of just guessing. |
Keywords
» Artificial intelligence » Bert » Feature selection » Large language model » Machine learning » Transformer
