Efficient Sentiment, Emotion and Toxic Speech Analysis on Social Media Using Lightweight LLMs: A Practical Approach for Digital Transformation

Social media generate massive amounts of unstructured, multilingual textual data that contain many sentiments, emotions and opinions from users that provide insights for enterprises undergoing digital transformation. In this paper, we evaluate a variety of commercially available, lightweight Large Language Models (LLMs) for sentiment, emotion, aspect-based sentiment, and toxic speech detection on noisy, real-world social media datasets. These lightweight models are compared (against traditional