Extract Hotel Reviews from Goibibo & MakeMyTrip for Enhanced Guest Insights 2025
                     Extract Hotel Reviews from 
Goibibo & MakeMyTrip for 
Enhanced Guest Insights 2025
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Introduction
The hospitality industry is increasingly reliant on online 
customer feedback to enhance service quality, drive 
bookings, and optimize hotel operations. Platforms like 
Goibibo and MakeMyTrip host millions of user-generated 
reviews that provide critical insights into guest experiences, 
preferences, and satisfaction levels. Businesses and analysts 
aiming for actionable insights can Extract Hotel Reviews from 
Goibibo & MakeMyTrip, which enables structured data 
collection across categories such as room quality, amenities, 
service, pricing, and overall experience.
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Using the Goibibo Guest Reviews Dataset, companies 
can analyze trends in customer feedback, detect emerging 
patterns in guest preferences, and assess service 
performance across multiple hotel segments. Goibibo Hotel 
Reviews Data Analytics further allows stakeholders to 
evaluate sentiment, rating distribution, and identify areas for 
operational improvements. Similarly, MakeMyTrip hosts a 
comprehensive MakeMyTrip Guest Reviews Dataset, 
capturing diverse insights across urban and tourist 
destinations in India. By implementing systematic extraction 
processes, organizations can generate real-time datasets 
supporting both strategic planning and competitive 
benchmarking.
Importance of Hotel Review Analysis
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Analyzing hotel reviews offers multiple benefits:
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• Customer Sentiment Understanding: Reviews reveal 
guest satisfaction, loyalty potential, and service gaps.
• Operational Optimization: Data-driven insights allow 
hotels to improve staff allocation, housekeeping 
efficiency, and amenity offerings.
• Pricing Strategy Insights: Reviews correlated with 
pricing provide an understanding of perceived value and 
price sensitivity.
• Competitor Benchmarking: Extracting reviews from 
multiple platforms enables hotels to compare 
performance with competitors.
• Marketing and Personalization: Positive reviews can 
be leveraged for promotions, while negative feedback 
informs service recovery strategies.
Platforms like Goibibo and MakeMyTrip allow rich 
granularity of feedback, including star ratings, textual \
comments, stay duration, room type, and traveler type. 
These elements form the foundation for structured method 
to Scrape MakeMyTrip Hotel Reviews Data initiatives, 
enabling deeper analysis of guest experiences.
Methodology for Hotel Review Extraction
Data extraction from hotel booking platforms requires 
careful planning and adherence to legal and ethical 
guidelines. The following steps outline a practical approach:
• Target Identification: List hotels and properties across 
desired cities or regions.
• Platform Selection: Identify source platforms such as 
Goibibo and MakeMyTrip.
• Data Collection Tools: Utilize web scraping 
frameworks, automated pipelines, and APIs.
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• Dataset Structuring: Organize collected data into 
structured tables including columns for hotel name, 
rating, review text, date, traveler type, and room 
category.
• Data Cleaning and Validation: Remove duplicates, 
standardize formats, and verify authenticity.
• Analysis Preparation: Aggregate sentiment scores, 
calculate review frequency, and identify key trends.
Hotels and hospitality consultants increasingly rely on 
Hotel Data Scraping Services to automate this process, 
ensuring real-time and scalable insights. Additionally, hotel 
customer feedback Data analytics allows firms to segment 
reviews by category, highlighting critical operational areas 
for improvement. \
Review Trends Across Goibibo and MakeMyTrip
Analysis of guest reviews reveals both commonalities and 
platform-specific trends. Goibibo reviews often emphasize 
price-to-value ratio and location convenience, whereas 
MakeMyTrip reviews highlight service quality and amenities.
Table 1: Average Ratings and Review Volume for Top 
5 Hotels (Sample Data 2025)
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Hotel Name Platform Average Total Reviews Key 
Rating Observation
High luxury 
The Leela Palace Goibibo 4.7 1,240 satisfaction, 
premium 
amenities
Excellent 
ITC Grand Chola MakeMyTrip 4.5 1,010 service, minor 
check-in delays
Exceptional 
Taj Mahal Hotel Goibibo 4.6 980 location, room 
size praised
Positive service 
Radisson Blu MakeMyTrip 4.3 870 feedback, dining 
rated lower
Affordable 
Lemon Tree 
Premier Goibibo 4.2 650
pricing, clean 
rooms 
emphasized
This table highlights that high-rated hotels consistently \
receive positive feedback on service and amenities. Lower-
rated aspects often relate to minor operational issues or 
pricing concerns.
Sentiment and Review Analysis
Using Real Time hotel review comparison, analysts can 
monitor sentiment trends across platforms, identifying 
patterns in guest satisfaction. Textual analysis reveals 
commonly mentioned themes such as room cleanliness, 
staff friendliness, breakfast quality, and Wi-Fi availability. 
Positive sentiment clusters around aspects like luxurious 
amenities, location convenience, and family-friendly 
services, whereas negative sentiment often concerns 
check-in delays, pricing perception, or maintenance issues.
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By leveraging hotel review extraction guide, companies can 
structure sentiment scoring and trend detection, enabling 
faster operational interventions. For example, hotels can 
prioritize addressing recurring complaints to improve 
ratings or promote positive feedback through marketing 
campaigns.
Pricing and Booking Trends
Extracted review datasets can be combined with pricing 
data to correlate guest perception with cost. 
Hotel Room Price Trends Dataset allows analysts to 
determine whether higher prices correspond with higher 
satisfaction or if certain amenities justify premium pricing.
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Table 2: Room Pricing vs Guest Satisfaction (Sample 
Data 2025)
Hotel Name Avg Room Avg Guest Booking Pricing Insight
Price (₹) Rating Volume
High price 
The Leela Palace 18,500 4.7 1,200 justified by 
luxury
ITC Grand Chola 15,800 4.5 950 Premium service supports pricing
Taj Mahal Hotel 16,200 4.6 900 Central location enhances value
Radisson Blu 12,500 4.3 850 Slightly lower 
ratings vs price
Lemon Tree Affordable 
Premier 8,200 4.2 600 pricing attracts volume
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Analysis shows that while premium hotels maintain high 
satisfaction levels, mid-range hotels must balance pricing 
and service quality to maintain competitiveness.
Applications of Hotel Review Extraction
Extracted review data supports multiple operational and 
strategic applications:
• Service Enhancement: Identify recurring complaints 
for staff training and operational improvements.
• Marketing Optimization: Highlight strengths in 
promotional campaigns to attract target audiences.
• Competitive Benchmarking: Compare ratings and 
feedback across multiple platforms to identify 
competitive advantages.
• Revenue Management: Align pricing strategies with \
perceived value to maximize profitability.
• Trend Forecasting: Predict seasonal or event-driven 
spikes in hotel demand using review and booking data.
Automation and API-based monitoring streamline the 
extraction of large volumes of data. Continuous updates 
enable hotels to respond to trends quickly, maintaining a 
positive guest experience.
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Conclusion
In today’s hospitality landscape, leveraging data from 
guest reviews is essential for operational efficiency and 
market intelligence. Integrating Hotel Data Intelligence
 allows hotels to monitor sentiment, detect emerging 
issues, and benchmark performance across platforms. 
Coupled with insights by Web Scraping hotel customer 
feedback, organizations can align pricing strategies with 
customer expectations. By systematically analyzing 
Goibibo and MakeMyTrip datasets, hotels gain a holistic 
view of performance, enabling smarter promotional 
campaigns, better inventory allocation, and improved guest 
satisfaction. Advanced analytics derived from real-time and 
historical datasets ensure that hotels remain competitive, 
responsive, and aligned with evolving consumer 
preferences, ultimately driving revenue growth and long-
term sustainability.
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