# Optimization Techniques for Sentiment Analysis Based on LLM (GPT-3)

arXiv: https://arxiv.org/abs/2405.09770 · HTML: https://mlsanity.com/paper/2405.09770/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 15 references that are in the map.
- First posted on arXiv 16 May 2024.

## Builds on (most cited first, 8 of 8)

- [How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings](https://mlsanity.com/paper/1909.00512/) (2019)
- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021)
- [Using cognitive psychology to understand GPT-3](https://mlsanity.com/paper/2206.14576/) (2022)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning](https://mlsanity.com/paper/2011.01403/) (2020)
- [Comparing Rewinding and Fine-tuning in Neural Network Pruning](https://mlsanity.com/paper/2003.02389/) (2020)
- [Regularization techniques for fine-tuning in neural machine translation](https://mlsanity.com/paper/1707.09920/) (2017)
- [Multi-granulariy Time-based Transformer for Knowledge Tracing](https://mlsanity.com/paper/2304.05257/) (2023)

## Built on by (most cited first, 7 of 7)

- [Refining Input Guardrails: Enhancing LLM-as-a-Judge Efficiency Through Chain-of-Thought Fine-Tuning and Alignment](https://mlsanity.com/paper/2501.13080/) (2025)
- [Harnessing the Potential of Large Language Models in Modern Marketing Management: Applications, Future Directions, and Strategic Recommendations](https://mlsanity.com/paper/2501.10685/) (2025)
- [A longitudinal sentiment analysis of Sinophobia during COVID-19 using large language models](https://mlsanity.com/paper/2408.16942/) (2024)
- [In-Context Learning for Long-Context Sentiment Analysis on Infrastructure Project Opinions](https://mlsanity.com/paper/2410.11265/) (2024)
- [Doppelg"anger's Watch: A Split Objective Approach to Large Language Models](https://mlsanity.com/paper/2409.06107/) (2024)
- [Emotion Alignment: Discovering the Gap Between Social Media and Real-World Sentiments in Persian Tweets and Images](https://mlsanity.com/paper/2504.10662/) (2025)
- [Leveraging Large Language Models for Sentiment Analysis: Multi-Modal Analysis of Decentraland's MANA Token](https://mlsanity.com/paper/2605.20192/) (2026)

## Abstract (arXiv)

With the rapid development of natural language processing (NLP) technology, large-scale pre-trained language models such as GPT-3 have become a popular research object in NLP field. This paper aims to explore sentiment analysis optimization techniques based on large pre-trained language models such as GPT-3 to improve model performance and effect and further promote the development of natural language processing (NLP). By introducing the importance of sentiment analysis and the limitations of traditional methods, GPT-3 and Fine-tuning techniques are introduced in this paper, and their applications in sentiment analysis are explained in detail. The experimental results show that the Fine-tuning technique can optimize GPT-3 model and obtain good performance in sentiment analysis task. This study provides an important reference for future sentiment analysis using large-scale language models.