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Comparison of VADER and TextBlob labeling for sentiment analysis using machine learning and deep learning models: A
Socheat In1, Sieb Chanchamnan2
1Department of Business Administration, Global Business School, Soonchunhyang University, Asan, Chungnam, Republic of Korea.
Acta Psychologica
|January 22, 2026
Summary
User engagement with generative AI like ChatGPT grows with practical utility and emotional connection. Sentiment analysis of 88,343 comments shows trust hinges on content quality, enhancing AI adoption.
Area of Science:
- Artificial Intelligence
- Human-Computer Interaction
- Natural Language Processing
Background:
- Generative AI (AI) is increasingly vital for service delivery and user engagement.
- Understanding user perceptions and emotional connections is key to AI adoption.
- This study analyzes user experiences with generative AI to identify drivers of acceptance.
Purpose of the Study:
- To investigate user experiences with generative AI through sentiment analysis.
- To identify key themes influencing user attraction and trust in AI.
- To evaluate the effectiveness of different sentiment analysis tools and machine learning models.
Main Methods:
- Sentiment analysis of 88,343 user comments.
- Topic modeling to identify key themes.
- Emotional analysis using VADER and TextBlob for sentiment labeling.
- Performance evaluation of machine learning (GRU, SVM) and deep learning classifiers.
Main Results:
- Key themes attracting users include idea generation, learning, support, and task assistance.
- Trust in ChatGPT correlates with perceived content quality and accuracy.
- TextBlob-labeled datasets improved classifier performance, with GRU achieving 96.38% accuracy.
- Advanced machine learning and deep learning models demonstrated superior predictive performance.
Conclusions:
- User acceptance of generative AI is influenced by practical utility, emotional engagement, and contextual relevance.
- Lexicon-based sentiment labeling methods enhance classification effectiveness.
- Human-AI interaction dynamics are shaped by a combination of functional and emotional factors.
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