---
title: "SentimentGPT — paper by Kheiri and Karimi (2023)"
description: "Tweet sentiment classification remains hard. 97 references, cited by 12 papers here. Among them: 40."
authors: ["Kiana Kheiri", "Hamid Karimi"]
year: 2023
venue: "arXiv:2307.10234"
source: "https://arxiv.org/abs/2307.10234"
doi: "10.48550/arxiv.2307.10234"
arxiv: "2307.10234"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/kheiri-2023-sentimentgpt-exploiting-gpt-advanced-sentiment/"
---
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# SentimentGPT: Exploiting GPT for Advanced Sentiment Analysis and its Departure from Current Machine Learning

**Authors:** Kiana Kheiri and Hamid Karimi · **Year:** 2023 · **Venue:** arXiv:2307.10234 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2307.10234) · [PDF](https://arxiv.org/pdf/2307.10234) · [DOI 10.48550/arxiv.2307.10234](https://doi.org/10.48550/arxiv.2307.10234) · [arXiv 2307.10234](https://arxiv.org/abs/2307.10234) · [OpenAlex](https://openalex.org/W4385015470)

**Citations:** 40 citations (OpenAlex, 2026-10-14) · [12](https://md.research.elamur.ai/free/en/papers/kheiri-2023-sentimentgpt-exploiting-gpt-advanced-sentiment/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Tweet sentiment classification remains hard. Posts are short and noisy, and they carry sarcasm, irony, emojis and abbreviations that classical classifiers often misread. Kheiri and Karimi (Utah State University) test whether OpenAI's GPT family can do better on the Twitter benchmark of SemEval 2017 Task 4. They compare three ways of using GPT:
- prompting GPT-3.5 Turbo with task instructions;
- fine-tuning GPT models on the labelled tweets;
- training a separate classifier on GPT embeddings.

The study asks three questions: how GPT compares with existing machine-learning systems, how individual GPT products differ, and whether GPT handles subtle cues such as emotion, emojis and mixed sentiment. The authors report that the GPT-based approaches beat the previous best results on this benchmark by over 22% in F1-score. A qualitative analysis suggests these models also cope better with context and sarcasm. The work is early evidence that general-purpose LLMs can outperform task-specific sentiment models, although it rests on a single English Twitter dataset.

## Key points
- Benchmark: SemEval 2017 Task 4 (Twitter sentiment analysis). The prior systems compared include SemEval-2017 entries such as DataStories, BB_twtr, TSA-INF, XJSA and ELiRF-UPV, plus BERTweet, judging by the references.
- Three GPT strategies were tested: prompt engineering with GPT-3.5 Turbo, fine-tuned GPT models, and classification on GPT embeddings.
- The headline result is an F1-score gain of more than 22% over the previous state of the art. Per-model scores were not in the provided excerpt.
- The authors include an error analysis of linguistic nuances (context, sarcasm, emojis, mixed sentiment), contrasting the best GPT model with earlier state-of-the-art systems.
- Code is released at github.com/DSAatUSU/SentimentGPT.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Utah State University](https://md.research.elamur.ai/free/en/universities/utah-state-university/) — University, Logan, United States · 2 works · 56 citations

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## Cite

Cite as: Kiana Kheiri and Hamid Karimi. 2023. SentimentGPT: Exploiting GPT for Advanced Sentiment Analysis and its Departure from Current Machine Learning. arXiv:2307.10234.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{kheiri2023sentimentgpt,
  title = {{SentimentGPT: Exploiting GPT for Advanced Sentiment Analysis and its Departure from Current Machine Learning}},
  author = {Kiana Kheiri and Hamid Karimi},
  year = {2023},
  doi = {10.48550/arxiv.2307.10234},
  eprint = {2307.10234},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2307.10234}
}
```

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## References

97 references, 21 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 30 October 2026*