LLMS FOR SENTIMENT ANALYSIS IN EDUCATION: A STUDY IN RESOURCE-LIMITED SETTINGS

Sentiment analysis is a computational technique employed to extract and interpret subjective information from textual data. It involves the identification and classification of sentiments, opinions, and emotions expressed within the text. By analyzing linguistic cues, such as word choice, syntax, and sentiment lexicons, sentiment analysis can discern a range of emotions, from positive to negative, as well as more nuanced sentiments, such as anger, joy, or surprise. This powerful tool has the potential to unlock valuable insights from vast amounts of unstructured text data, which enables informed decision-making and effective communication in various domains, including education.

Recent advances in sentiment analysis have leveraged the power of deep neural networks, particularly general-purpose Large Language Models (LLMs) trained on extensive labeled datasets. However, real-world applications frequently encounter challenges related to the availability of large, high-quality labeled data and the computational resources necessary for training such models.

This research addresses these challenges by investigating effective strategies for utilizing LLMs in scenarios with limited data and computational resources. Specifically, this study explores three techniques: zero-shot learning, N-shot learning and fine-tuning. By evaluating these methods, this research aims to demonstrate the feasibility of employing general-purpose LLMs for sentiment analysis within educational contexts even when access to computational resources and labeled data is limited. The findings of this study reveal that different adaptation methods lead to significantly different LLM performance outcomes.