Student extracurricular activities play an important role in enriching the students' educational experiences. With the increasing popularity of Machine Learning and Natural Language Processing, it becomes a logical step that incorporating ML-NLP in improving extracurricular activities is a potential focus of study in Artificial Intelligence (AI). This research study aims to develop a machine learning workflow that will quantify the effectiveness of student-organized activities based on student emotional responses using sentiment analysis. The study uses the Bidirectional Encoder Representations from Transformers (BERT) Large Language Model (LLM) called via the pysentimiento toolkit, as a Transformer pipeline in Hugging Face. A sample data set from Organization C, a Recognized Student Organization (RSO) of a higher educational institute in the Philippines, College X, was used to develop the workflow. The workflow consisted of data preprocessing, key feature selection, LLM feature processing, and score aggregation, resulting in an Event Score for each data set. The results show that the BERT LLM can also be used effectively in analyzing sentiment beyond product reviews and post comments. For the student affairs offices of educational institutions, this study can provide a practical example of how NLP can be applied to real-world scenarios, showcasing the potential impact of data-driven decision making.

翻译：学生课外活动在丰富学生教育经历方面发挥着重要作用。随着机器学习和自然语言处理技术的日益普及，将ML-NLP技术应用于改进课外活动成为人工智能（AI）研究的一个潜在焦点。本研究旨在开发一种机器学习工作流程，通过情感分析基于学生情绪反馈来量化学生组织活动的有效性。该研究采用通过pysentimiento工具包调用的双向编码器表示转换器（BERT）大语言模型（LLM），作为Hugging Face中的Transformer流水线。研究使用来自菲律宾某高等教育机构X学院认可学生组织（RSO）——C组织的样本数据集来构建工作流程。该工作流程包括数据预处理、关键特征选择、LLM特征处理和分数聚合，最终为每个数据集生成事件评分。结果表明，BERT LLM不仅能有效分析产品评论和帖子评论的情感，还可拓展至其他领域。对于教育机构的学生事务办公室而言，本研究为如何将NLP技术应用于实际场景提供了实践案例，展示了数据驱动决策的潜在影响力。