利用自然语言处理量化学生组织活动的有效性
Quantifying the Effectiveness of Student Organization Activities using Natural Language Processing
AI总结：
本研究提出一种基于BERT大语言模型情感分析的机器学习工作流程，通过数据预处理、特征选择、LLM处理和分数聚合来量化学生组织活动有效性，为教育机构数据驱动决策提供实践范例。
摘要：
学生课外活动在丰富学生教育体验方面发挥着重要作用。随着机器学习与自然语言处理日益普及，将机器学习-自然语言处理融入课外活动改进，成为人工智能领域一个潜在的研究重点。本研究旨在开发一种机器学习工作流程，基于学生情绪反应，利用情感分析来量化学生组织活动的有效性。该研究使用通过 pysentimiento 工具包调用的双向编码器表示变换器（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 College X 的认证学生组织（RSO）Organization C 的样本数据集来开发该工作流程。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一个活动得分（Event Score）。结果表明，BERT LLM 也能有效用于分析产品评论和帖子评论之外的情感。对于教育机构的学生事务办公室而言，本研究可提供一个实际示例，展示自然语言处理如何应用于现实场景，并体现数据驱动决策的潜在影响。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
