Insightful Emotion Extractor - Sentiment Analyzer using LLMs

Abstract

Sentiment analysis is a crucial part of natural language processing (NLP) that categorizes text into positive, negative, or neutral sentiments. This technology helps businesses automatically interpret customer emotions, enabling informed decisions in marketing, product development, and customer support. Traditional machine learning models like Random Forest, Naive Bayes, and Support Vector Machine (SVM) have been commonly used for sentiment classification, especially effective for short and concise texts such as brief customer comments. SVM, in particular, performs well in classifying sentiments in short texts. However, the emergence of advanced large language models (LLMs) like GPT-4 has revolutionized sentiment analysis by capturing more nuanced emotional expressions. GPT-4 excels in understanding complex context, sarcasm, and subtle emotional tones within longer texts, offering multi-dimensional sentiment insights such as satisfaction, frustration, and uncertainty. This makes it more capable than traditional models when analyzing detailed customer reviews or conversations. Comparative studies reveal that while traditional models handle short texts efficiently, GPT-4 surpasses them in analyzing in-depth content with greater precision, recall, and F1 scores. GPT-4 also uniquely identifies mixed sentiments that simpler models often miss. In conclusion, although traditional machine learning approaches remain useful for straightforward sentiment analysis tasks, GPT-4 provides a more sophisticated and comprehensive understanding of customer emotions in complex texts. Integrating GPT-4 into sentiment analysis workflows can significantly enhance the accuracy and richness of insights derived from customer feedback, supporting better business decisions.