July 19, 2025

Fine-Tuning a Transformer Model for Sentiment Analysis and Summarization of ISO-Certified Customer Satisfaction Surveys

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BERT achieved the highest accuracy of 84% in analyzing customer satisfaction surveys, outperforming LSTM and Logistic Regression.
LSTM recorded a performance of 78%, while Logistic Regression lagged with an accuracy of only 39%.
Machine learning and deep learning techniques were employed to automate sentiment analysis of open-ended survey responses.
This research emphasizes the potential of advanced models like BERT in processing unstructured customer feedback for service improvement.

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Implication

Comparative analysis shows transformer algorithms enhance sentiment analysis accuracy in ISO-certified institutions, suggesting benefits for service quality.

Cite This Study

Daluyon et al. (2025) studied this question.

synapsesocial.com/papers/68af73697567bf4f94fed5fb https://doi.org/10.69478/best2025v1n2a025

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Implication

Comparative analysis shows transformer algorithms enhance sentiment analysis accuracy in ISO-certified institutions, suggesting benefits for service quality.

Fine-Tuning a Transformer Model for Sentiment Analysis and Summarization of ISO-Certified Customer Satisfaction Surveys

Key Points

Discussion

Implication

Cite This Study

Fine-Tuning a Transformer Model for Sentiment Analysis and Summarization of ISO-Certified Customer Satisfaction Surveys

Key Points

Discussion

Implication

Cite This Study

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