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

arXiv: https://arxiv.org/abs/2409.09989 · HTML: https://mlsanity.com/paper/2409.09989/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 9 references that are in the map.
- First posted on arXiv 16 Sep 2024.

## Builds on (most cited first, 3 of 3)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [An Explainable Transformer-based Model for Phishing Email Detection: A Large Language Model Approach](https://mlsanity.com/paper/2402.13871/) (2024)

## Built on by (most cited first, 8 of 8)

- [A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directions](https://mlsanity.com/paper/2409.16430/) (2024)
- [Jailbreak-AudioBench: In-Depth Evaluation and Analysis of Jailbreak Threats for Large Audio Language Models](https://mlsanity.com/paper/2501.13772/) (2025)
- [Comprehensive Framework for Evaluating Conversational AI Chatbots](https://mlsanity.com/paper/2502.06105/) (2025)
- [LUSIFER: Language Universal Space Integration for Enhanced Multilingual Embeddings with Large Language Models](https://mlsanity.com/paper/2501.00874/) (2025)
- [Analyzing User Perceptions of Large Language Models (LLMs) on Reddit: Sentiment and Topic Modeling of ChatGPT and DeepSeek Discussions](https://mlsanity.com/paper/2502.18513/) (2025)
- [Learning to Hear by Seeing: It's Time for Vision Language Models to Understand Artistic Emotion from Sight and Sound](https://mlsanity.com/paper/2511.12077/) (2025)
- [Emotion Alignment: Discovering the Gap Between Social Media and Real-World Sentiments in Persian Tweets and Images](https://mlsanity.com/paper/2504.10662/) (2025)
- [MultiFluxAI Enhancing Platform Engineering with Advanced Agent-Orchestrated Retrieval Systems](https://mlsanity.com/paper/2508.21307/) (2025)

## Abstract (arXiv)

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.