mlsanityData to 7 Oct 2026

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

arXiv 2405.09770 ↗First posted 16 May 2024cs.CL cs.AIcited by 7

Not on the map. Why?

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.

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

Builds on

8 of 15 references.

References · 8

  1. 2019How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddingscited by 770
  2. 2021Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Surveycited by 457
  3. 2022Using cognitive psychology to understand GPT-3cited by 389
  4. 2020BERTweet: A pre-trained language model for English Tweetscited by 346
  5. 2020Supervised Contrastive Learning for Pre-trained Language Model Fine-tuningcited by 275
  6. 2020Comparing Rewinding and Fine-tuning in Neural Network Pruningcited by 238
  7. 2017Regularization techniques for fine-tuning in neural machine translationcited by 27
  8. 2023Multi-granulariy Time-based Transformer for Knowledge Tracingcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 3; 2026: 1.

7 papers.

  1. 2025Refining Input Guardrails: Enhancing LLM-as-a-Judge Efficiency Through Chain-of-Thought Fine-Tuning and Alignmentcited by 14
  2. 2025Harnessing the Potential of Large Language Models in Modern Marketing Management: Applications, Future Directions, and Strategic Recommendationscited by 2
  3. 2024A longitudinal sentiment analysis of Sinophobia during COVID-19 using large language modelscited by 1
  4. 2024In-Context Learning for Long-Context Sentiment Analysis on Infrastructure Project Opinionscited by 1
  5. 2024Doppelg"anger's Watch: A Split Objective Approach to Large Language Modelscited by 0
  6. 2025Emotion Alignment: Discovering the Gap Between Social Media and Real-World Sentiments in Persian Tweets and Imagescited by 0
  7. 2026Leveraging Large Language Models for Sentiment Analysis: Multi-Modal Analysis of Decentraland's MANA Tokencited by 0