Version 1
: Received: 26 April 2023 / Approved: 27 April 2023 / Online: 27 April 2023 (04:30:17 CEST)
How to cite:
Wickramasinghe, T.; Fernando, P. A Review on Deriving Maintenance of Accommodations Via Text-based Feedback Analysis. Preprints2023, 2023041035. https://doi.org/10.20944/preprints202304.1035.v1
Wickramasinghe, T.; Fernando, P. A Review on Deriving Maintenance of Accommodations Via Text-based Feedback Analysis. Preprints 2023, 2023041035. https://doi.org/10.20944/preprints202304.1035.v1
Wickramasinghe, T.; Fernando, P. A Review on Deriving Maintenance of Accommodations Via Text-based Feedback Analysis. Preprints2023, 2023041035. https://doi.org/10.20944/preprints202304.1035.v1
APA Style
Wickramasinghe, T., & Fernando, P. (2023). A Review on Deriving Maintenance of Accommodations Via Text-based Feedback Analysis. Preprints. https://doi.org/10.20944/preprints202304.1035.v1
Chicago/Turabian Style
Wickramasinghe, T. and Pumudu Fernando. 2023 "A Review on Deriving Maintenance of Accommodations Via Text-based Feedback Analysis" Preprints. https://doi.org/10.20944/preprints202304.1035.v1
Abstract
In today's business world, many companies have realized that consumer feedback evaluations play a crucial part in shaping the company's future activities. The hotel and tourism industries are well-known examples where the user feedback has become crucial. Even though most accommodations want their clients to express their opinions about their services, these reviews are nevertheless carefully reviewed by them. This manual approach is time demanding and error prone, the level of expertise of the individual analyzing the evaluations determines the quality of the manual analysis. This research focuses on the development of a tool-based support system that analyzes text-based customer evaluations and then highlights the maintenance issues identified in the reviews, removing the need to manually evaluate and analyze the customer reviews. The process includes data preparation, sentiment analysis, and a fully trained deep learning model to extract critical insights. Using sentiment analysis techniques after data preprocessing and trained deep learning model, an accuracy of 96\% was achieved, and this paper discusses how this technology helps to meet the needs of accommodation management by completing activities efficiently. This might save management time and helps to save money without losing clients, allowing them to enhance their additional sales.
Keywords
Machine Learning; Classification; Natural Language Processing; Text-based Customer Review Analysis; Sentiment Analysis; Deep Learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
