TY  - JOUR
AU  - Bakan, Berat
AU  - Karcioglu, Abdullah Ammar
AU  - Katırcıoğlu, Esra
PY  - 2026
DA  - 2026/09/22
TI  - Detecting Sentiment Analysis Correlation Between Hotel Employee and Customer Reviews Using Machine Learning Algorithms
JO  - ICCK Transactions on Emerging Topics in Artificial Intelligence
T2  - ICCK Transactions on Emerging Topics in Artificial Intelligence
JF  - ICCK Transactions on Emerging Topics in Artificial Intelligence
VL  - 3
IS  - 3
SP  - 188
EP  - 201
DO  - 10.62762/TETAI.2026.991738
UR  - https://www.icck.org/article/abs/TETAI.2026.991738
KW  - sentiment analysis
KW  - multi-source correlation detection
KW  - semi-supervised preprocessing
KW  - topic modeling
KW  - service intelligence
AB  - Analyzing heterogeneous online reviews from multiple stakeholder groups represents an emerging challenge in AI-driven service intelligence. This study proposes a two-stage sentiment correlation detection framework and applies it to customer and employee reviews of the Istanbul Marriott \c{S}i\c{s}li hotel. During dataset construction, a semi-supervised domain-specific blacklisting approach was developed alongside standard preprocessing steps to improve sentiment signal quality. In the first phase, customer and employee reviews were treated as separate datasets, and 5-fold cross-validation was applied using TF-IDF, BOW, and Word2Vec representations with multiple classifiers, achieving 99.8% and 93.8% F1-scores respectively. In the second phase, correlation analysis on the combined dataset yielded a 97.1% F1-score. In the final phase, LDA-based topic modeling identified three topic clusters, with the topic-based model achieving a 97.5% F1-score. The findings demonstrate that customer and employee perceptions converge at the macro level but diverge on a topic-by-topic basis. The proposed framework offers a generalizable approach for multi-source sentiment correlation analysis in domain-specific AI applications.
SN  - 3068-6652
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Bakan2026Detecting,
  author = {Berat Bakan and Abdullah Ammar Karcioglu and Esra Katırcıoğlu},
  title = {Detecting Sentiment Analysis Correlation Between Hotel Employee and Customer Reviews Using Machine Learning Algorithms},
  journal = {ICCK Transactions on Emerging Topics in Artificial Intelligence},
  year = {2026},
  volume = {3},
  number = {3},
  pages = {188-201},
  doi = {10.62762/TETAI.2026.991738},
  url = {https://www.icck.org/article/abs/TETAI.2026.991738},
  abstract = {Analyzing heterogeneous online reviews from multiple stakeholder groups represents an emerging challenge in AI-driven service intelligence. This study proposes a two-stage sentiment correlation detection framework and applies it to customer and employee reviews of the Istanbul Marriott \c{S}i\c{s}li hotel. During dataset construction, a semi-supervised domain-specific blacklisting approach was developed alongside standard preprocessing steps to improve sentiment signal quality. In the first phase, customer and employee reviews were treated as separate datasets, and 5-fold cross-validation was applied using TF-IDF, BOW, and Word2Vec representations with multiple classifiers, achieving 99.8\% and 93.8\% F1-scores respectively. In the second phase, correlation analysis on the combined dataset yielded a 97.1\% F1-score. In the final phase, LDA-based topic modeling identified three topic clusters, with the topic-based model achieving a 97.5\% F1-score. The findings demonstrate that customer and employee perceptions converge at the macro level but diverge on a topic-by-topic basis. The proposed framework offers a generalizable approach for multi-source sentiment correlation analysis in domain-specific AI applications.},
  keywords = {sentiment analysis, multi-source correlation detection, semi-supervised preprocessing, topic modeling, service intelligence},
  issn = {3068-6652},
  publisher = {Institute of Central Computation and Knowledge}
}