TY - GEN
T1 - Bridging the Gap
T2 - 26th International Conference on Artificial Intelligence and Applications, ICAI 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024
AU - Haider, Maliha
AU - Hu, Bin
AU - Kwak, Daehan
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - Customer reviews, encompassing both textual descriptions and star ratings, offer valuable insights into customer satisfaction with products and services. While sentiment analysis tools determine whether text is positive, negative, or neutral, their output often differs from the nuanced evaluations conveyed by user-provided star ratings. This mismatch poses a challenge when attempting to correlate sentiment analysis results with traditional star rating systems. This study aims to investigate the correlation between the sentiment in text reviews and their corresponding numeric star ratings. By examining the alignment between these two, we assess whether the sentiment of text reviews accurately resembles the corresponding user-assigned star ratings. Additionally, this research focuses on developing machine learning models to predict star ratings from textual reviews, offering a valuable tool for products or businesses on platforms that lack native rating systems.
AB - Customer reviews, encompassing both textual descriptions and star ratings, offer valuable insights into customer satisfaction with products and services. While sentiment analysis tools determine whether text is positive, negative, or neutral, their output often differs from the nuanced evaluations conveyed by user-provided star ratings. This mismatch poses a challenge when attempting to correlate sentiment analysis results with traditional star rating systems. This study aims to investigate the correlation between the sentiment in text reviews and their corresponding numeric star ratings. By examining the alignment between these two, we assess whether the sentiment of text reviews accurately resembles the corresponding user-assigned star ratings. Additionally, this research focuses on developing machine learning models to predict star ratings from textual reviews, offering a valuable tool for products or businesses on platforms that lack native rating systems.
KW - Machine Learning
KW - Natural Language Processing
KW - Pearson Correlation
KW - Sentiment Analysis
KW - Star Rating Prediction
UR - https://www.scopus.com/pages/publications/105005256594
U2 - 10.1007/978-3-031-86623-4_20
DO - 10.1007/978-3-031-86623-4_20
M3 - Conference contribution
AN - SCOPUS:105005256594
SN - 9783031866227
T3 - Communications in Computer and Information Science
SP - 241
EP - 252
BT - Artificial Intelligence and Applications - 26th International Conference, ICAI 2024, Held as Part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024, Revised Selected Papers
A2 - Arabnia, Hamid R.
A2 - Deligiannidis, Leonidas
A2 - Amirian, Soheyla
A2 - Shenavarmasouleh, Farzan
A2 - Ghareh Mohammadi, Farid
A2 - de la Fuente, David
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 22 July 2024 through 25 July 2024
ER -