Published in: Journal of Computer Science and Engineering in Innovations and Research (ISSN No: 3049-1762 online)
Publication Date: June 15, 2025
This research focuses on analyzing hotel reviews to determine the sentiment of customers using natural language processing (NLP) techniques. This work analysis the hotel reviews tweets as positive, negative, or neutral. The study includes a comprehensive review of existing literature, implementation of sentiment analysis models, and evaluation of results through various metrics. By employing machine learning algorithms and text analysis tools, this project endeavors to improve the understanding of customer feedback, guiding hotels in making datadriven decisions for better service delivery. The findings highlight the importance of sentiment analysis in the hospitality industry, offering actionable recommendations for leveraging customer reviews to foster a positive guest experience. This project ultimately contributes to the development of a more responsive and customer-focused hotel management strategy.
Sentiment Analysis, NLP, Machine Learning
As the digital age transforms various industries, the hospitality sector is not left behind. Customer reviews are pivotal in shaping the reputation and success of hotels. With the surge of usergenerated content, analyzing these reviews can provide profound insights into customer satisfaction and areas requiring improvement. This study explores the homophily effect in social media perception analysis, where individuals tend to associate with like-minded people, influencing their opinions and perceptions on various topics discussed online[1].
This project applies sentiment analysis to hotel reviews, categorizing them as positive, negative, or neutral to help hotel management understand customer feedback and inform data-driven decisions to enhance guest experiences. Sentiment analysis revolves around the complex task of accurately deciphering human emotions and opinions from text. This involves addressing complexities such as sarcasm, slang, and context -dependent meanings, which can significantly influence the sentiment classification. By analyzing a comprehensive dataset of hotel reviews, the project seeks to uncover trends and patterns that can guide hotel management in improving their services. This study explores various sentiment analysis techniques for social media posts, where people share their opinions, to understand and categorize sentiments expressed online[2].
In summary, this project explores the intersection of customer feedback and technological innovation, providing valuable insights that can drive improvements in the hospitality industry