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Journals / Acta Infologica / 2026 / Volume: 8 Issue: 1

Sentiment Analysis on GPT-4 with Comparative Models Using Twitter Data

Mustafa Özel(BURDUR MEHMET AKİF ERSOY UNIVERSITY, GRADUATE SCHOOL OF SOCIAL SCIENCES)0009-0001-7384-7486
Özlem Çetinkaya Bozkurt(MEHMET AKIF ERSOY UNIVERSITY, BUCAK FACULTY OF BUSINESS ADMINISTRATION, DEPARTMENT OF BUSINESS ADMINISTRATION)0000-0002-6218-2570
Pages
—
Publication Date
2024-06-28
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

All parameter findings indicate dependable machine learning, emphasizing the models’ success in classifying tweet sentiment.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Every day, people from all over the world use Twitter to talk about many different topics using hashtags.
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Key Findings & Data:This research aims to find out what Turkish Twitter users think about OpenAI’s latest AI model called Generative Pre-trained Transformer 4 (GPT-4).
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Conclusion & Impact:All parameter findings indicate dependable machine learning, emphasizing the models’ success in classifying tweet sentiment.
🔬Design:Modeling & Simulation
📍Scope:2023 Verileri
⚙️Variables:Sentiment analysis · social media · Twitter

Abstract

Every day, people from all over the world use Twitter to talk about many different topics using hashtags. Since ChatGPT was launched, researchers have been studying how people perceive it in society. This research aims to find out what Turkish Twitter users think about OpenAI’s latest AI model called Generative Pre-trained Transformer 4 (GPT-4). The quantitative data used in this study consist of hashtags on the topic of GPT-4 and involve 2,978 tweets on this topic that were shared on Twitter between March 14-April 9, 2023. The study uses TextBlob sentiment scores to classify the tweets and support vector machines, logistic regression, XGBoost, and random forest algorithms to classify the sentiment of the dataset. The results from the logistic regression, XGBoost, and support vector methods are in close alignment. All parameter findings indicate dependable machine learning, emphasizing the models’ success in classifying tweet sentiment.

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Cite This Article

Özel, M., Bozkurt, Ö. Ç. (2024). Sentiment Analysis on GPT-4 with Comparative Models Using Twitter Data. Acta Infologica, Volume: 8 Issue: 1. https://doi.org/10.26650/acin.1418834

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