Sentiment Analysis of Customer Reviews for a Leading Hotel Chain:
A Case Study

The Story

In the dynamic world of hospitality, where statistics show that 80% of travelers consult reviews before booking a hotel and unanswered feedback can lead to a 15% increase in customer churn, customer feedback is nothing short of invaluable. Amidst the vast sea of customer data, the challenge lies in deciphering its true potential.


Our client, a trailblazing hotel chain headquartered in Texas, stands as an industry giant since its founding in 1968. They boast an impressive portfolio of over 800 properties, both owned and franchised, across the United States, Canada, Mexico, and Honduras.


They engaged us to harness the power of data and sentiment analysis to unlock the insights hidden within guest reviews. The goal is to elevate service quality, empower strategic decision-making, and maintain a competitive edge in an ever-evolving landscape.

The Impact?

0 M+


0 %

Accuracy in Analyzing

Customer Feedback










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Breaking down the building process-
The Challenge

The foremost challenge was the management and analysis of an overwhelming volume of customer feedback data obtained from sources like TripAdvisor and Expedia. This data had to further be categorized into relevant categories, such as food, service, personnel, and more, which was both intricate and time-consuming.


Additionally, the challenge extended to decoding the nuanced sentiments expressed in these reviews. The ultimate objective was to transform this vast dataset into actionable insights that could inform the client’s business decisions and expansion strategies effectively.

Our Path to the Solution

Algoscale provided a strategic solution by leveraging innovative and cutting-edge methodologies.


  • Unsupervised learning: Our experts implemented a solution based on unsupervised learning, i.e. generic in all cases without any rule-based model, to analyze diverse customer feedback.
  • Data processing: Next, the data was processed and stored in a data lake using Apache Spark for accurate analysis.
  • Competitor analysis: We conducted a thorough competitor analysis, uncovering differences in customer sentiments and the impact of competitor events.
  • Sentiment analysis: Our experts leveraged advanced techniques, including cosine similarity, Word2vec, and lemmatization to accurately gauge the sentiment expressed in customer reviews.
  • Visualization: Several BI tools were employed for effective data visualization, making insights easily accessible and actionable.

Tech Stack

Paving the way to Success
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