Browsing by Author "Berrazueta, Juan Andres Coba"
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- ANALYSIS OF THE REAL IMPACT OF SOCIAL MEDIA AND ONLINE REPUTATION TO IMPROVE MARKETING STRATEGIES IN A HOTEL CHAINPublication . Berrazueta, Juan Andres Coba; Craveiro, Olga Marina Freitas; Sousa, Márcia Cristina Santos ViegasThe main objective of this research is to design and implement a comprehensive framework that integrates text mining, sentiment analysis, and Business Intelligence (BI) for the analysis of hotel reviews. The study aims to provide hotel managers with a systematic and automated tool capable of transforming unstructured textual data into actionable insights that improve customer satisfaction, enhance online reputation, and support data-driven marketing and operational strategies. This thesis investigates the integration of sentiment analysis, text mining, and BI frameworks as a strategic tool for online reputation management in the hospitality industry. The study combines a systematic literature review, conducted under the PRISMA guidelines, with an empirical project developed according to the CRISP-DM process model. The dataset used comprises all the positive and negative reviews from multiple sources—including Google Reviews, Booking.com, Tripadvisor, and physical surveys—covering five hotels in Portugal during 2023 and 2024. The methodology involved a pipeline of data preparation, including cleaning, deduplication, translation into European Portuguese, normalization, stemming, and lemmatization. Supervised machine learning models, particularly Logistic Regression and Naive Bayes, were implemented and optimized through techniques such as SMOTE and threshold adjustment, demonstrating high accuracy and strong recall for negative comments. Additionally, topic modeling (LDA and NMF) and semantic categorization were applied to extract latent themes and classify reviews into business-relevant categories. Results were operationalized through interactive dashboards in Power BI, which enabled the visualization of satisfaction levels, temporal trends, word frequencies, and category distributions across hotels. These dashboards provided to hotel managers with actionable insights to detect strengths, weaknesses, and seasonal patterns in customer perception. The system was further enhanced with an automated scraping pipeline for Google Reviews, ensuring continuous integration of updated customer feedback. The findings confirm that sentiment analysis and BI tools represent a powerful combination for transforming unstructured textual data into actionable insights. The study demonstrates the feasibility, scalability, and strategic relevance of this approach, while also highlighting limitations related to data availability and semantic overlaps. Ultimately, this work contributes to advancing data-driven decision-making in the hospitality industry.