Comprehensive Study on Sentiment Analysis: From Rule-based to modern LLM based system

TL;DR

This paper surveys the evolution of sentiment analysis from traditional rule-based methods to modern large language model-based systems, highlighting key challenges, state-of-the-art approaches, and future research directions in AI and NLP.

Contribution

It provides a comprehensive overview of sentiment analysis evolution, emphasizing the transition to LLMs and identifying future research opportunities in the field.

Findings

Transition from lexicon-based to deep learning methods

Challenges include bilingual texts and sarcasm detection

Emerging trends involve LLMs and bias mitigation

Abstract

This paper provides a comprehensive survey of sentiment analysis within the context of artificial intelligence (AI) and large language models (LLMs). Sentiment analysis, a critical aspect of natural language processing (NLP), has evolved significantly from traditional rule-based methods to advanced deep learning techniques. This study examines the historical development of sentiment analysis, highlighting the transition from lexicon-based and pattern-based approaches to more sophisticated machine learning and deep learning models. Key challenges are discussed, including handling bilingual texts, detecting sarcasm, and addressing biases. The paper reviews state-of-the-art approaches, identifies emerging trends, and outlines future research directions to advance the field. By synthesizing current methodologies and exploring future opportunities, this survey aims to understand sentiment analysis in the AI and LLM context thoroughly.