Ferryhopper vs Google & TripAdvisor Reviews Data Scraping
                     Ferryhopper vs Google & TripAdvisor Reviews Data Scraping: A Comprehensive 
Comparative Analysis
Introduction
Online reviews are a big deal for travelers picking ferry services. They share real experiences 
that help others decide where to book. This report dives into Ferryhopper vs Google & 
TripAdvisor reviews data scraping, which means collecting user feedback from these platforms 
to understand what people think about Ferryhopper, a popular app for booking ferries in places 
like Greece, Italy, Spain, and Croatia. Ferryhopper makes it easy to compare routes and prices, 
but reviews show what’s working and what’s not. By using a competitive review data scraper for 
ferry booking platforms, we can automatically gather this feedback to see how Ferryhopper 
stacks up against competitors like Direct Ferries. When we extract customer review comparison 
Google vs TripAdvisor, we notice Google reviews are short and often about port experiences, 
while TripAdvisor reviews are longer and focus on the booking process or ferry trips. This report 
explains how scraping works, shares two tables with sample data (based on September 2025 
searches), and shows why this helps travelers and ferry companies. It’s all about turning reviews 
into useful insights.
What Is Ferryhopper?
Overview of Ferryhopper’s Services
Ferryhopper, started in 2015 in Athens, Greece, is an app and website that simplifies booking 
ferry tickets. It works with over 190 ferry companies, covering more than 3,000 routes across 
the Mediterranean and Europe. For example, you can book a trip from Athens to Mykonos with 
Blue Star Ferries (2.5 hours, €40-€60) or from Split to Hvar in Croatia with Jadrolinija (45 
minutes, €10-€20). The app lets users check real-time schedules, compare prices, and book 
multi-leg trips, like hopping between Greek islands. People love its user-friendly design, but 
some complain about issues like slow refunds or app glitches. Scrape Ferryhopper ferry routes 
data and schedules to pull details like departure times, prices, and ferry operators from the site. 
This data helps businesses understand which routes are popular (e.g., high demand for 
Santorini in summer) or problematic (e.g., delays during windy seasons) when paired with 
review data.
Why Scrape Ferryhopper Data?
Scraping Ferryhopper’s routes and schedules reveals available trips, like multiple daily Athens-
Santorini sailings, helping predict busy seasons. Pairing this with reviews shows if customers are 
satisfied or face issues like cancellations. This data helps ferry companies plan better, such as 
optimizing schedules, and lets travelers choose reliable routes. Scraping also compares 
Ferryhopper to competitors like Direct Ferries, highlighting strengths like its user-friendly app. 
Scrape Ferryhopper ferry routes data and schedules to provide a clear view of offerings, 
enabling smarter decisions. By combining route data with feedback, businesses can improve 
services, and travelers can make informed choices, ensuring smoother trips and better 
experiences across popular Mediterranean routes.
Understanding Google and TripAdvisor Reviews
Google Maps Reviews Explained
Google Maps hosts reviews tied to physical locations, like ports or ferry offices, or even 
Ferryhopper’s online presence. These reviews are usually short, about 50 words, and focus on 
quick experiences, like “Got my ticket fast at Piraeus port!” or “App was easy to use, 5 stars!” 
They’re often written on the go, so they capture real-time feelings. Scrape Google Maps listed 
ferry travel reviews to collect these comments, star ratings, and dates from port pages or 
business listings. For example, scraping Piraeus port reviews might show feedback about 
Ferryhopper’s ticket pickup process. Google’s reviews are great for seeing what people think 
about specific locations or quick interactions with the service.
TripAdvisor Reviews Explained
TripAdvisor is different—it’s a travel platform with detailed reviews, often in forums or tied to 
attractions like ferry trips. Reviews here average 150 words and dive into the booking process, 
like “Ferryhopper’s app crashed during payment, but support fixed it.” Extract TripAdvisor ferry 
booking customer feedback pulls these longer stories, which often mention specific ferry 
operators or routes, like Blue Star Ferries or Athens-Mykonos. TripAdvisor’s forums also let 
users discuss experiences, giving deeper insights into what went right or wrong. These reviews 
are great for understanding the full customer journey, from booking to boarding.
Why Compare Google and TripAdvisor?
Google Maps and TripAdvisor offer distinct perspectives on customer experiences with ferry 
services like Ferryhopper. Google’s reviews are typically short, around 50 words, capturing quick 
impressions, such as “Easy app, booked Piraeus-Mykonos in minutes!” These often focus on port 
experiences or app usability, reflecting spontaneous feedback from travelers. In contrast, 
TripAdvisor’s reviews are longer, averaging 150 words, and dive into detailed stories, often 
highlighting issues like refund delays or booking glitches, such as “Ferryhopper’s support was 
slow to refund my canceled trip.” Extract TripAdvisor ferry booking customer feedback to reveal 
deeper insights into the booking process, complementing Google’s brief snapshots. Scraping 
both platforms provides a complete picture of user sentiments, from quick praises to in-depth 
complaints. However, scraping must follow strict rules like GDPR to protect user privacy. This 
means anonymizing data, like removing names, and using it ethically to avoid legal issues. By 
combining these insights, Ferryhopper can address pain points, like improving refund processes, 
and enhance strengths, such as its user-friendly app.
How Scraping Works?
Scraping Tools and Techniques
Scraping is like sending a robot to visit websites and copy information. For the Ferryhopper ferry 
reviews data scraping service, we use tools like Python’s BeautifulSoup to grab review text or 
Selenium to handle tricky pages that load slowly. The process has three steps: (1) find the 
review pages (like Google Maps’ port listings or TripAdvisor’s forums), (2) collect data like 
comments, ratings, and dates, and (3) clean it up for analysis, like removing duplicates. For 
example, scraping Google Maps for Piraeus port might pull 1,000 reviews, including ones about 
Ferryhopper’s voucher system. TripAdvisor scraping might grab 500 forum posts about booking 
experiences. These tools help gather hundreds of reviews quickly, saving time compared to 
reading them manually.
Challenges in Scraping
Scraping isn’t always easy. Websites like Google use CAPTCHA or slow loading to stop scrapers, 
so we use proxies (fake IP addresses) to keep going. TripAdvisor’s forums require clicking to see 
all comments, which needs advanced tools like Selenium. There are also legal challenges—laws 
like GDPR or website rules (robots.txt) mean we must scrape carefully, avoiding personal data 
and respecting limits. For example, we anonymize user names in reviews to protect privacy. 
After scraping, data is saved in tables or files, ready for analysis to find patterns like complaints 
about app crashes.
Selecting the right technologies is vital for effective promo and price tracking in the Q-
commerce sector. Solutions like those from iWeb Data Scraping enable rapid, scalable, and 
accurate tracking:
• Real-Time Q-Commerce Promo Tracking API: Automates offer, price, and discount data 
collection across top instant delivery platforms
• Quick Commerce Data Scraping Tools: Scrape product details, reviews, and pricing for 
holistic offer analysis 
• Quick Commerce Datasets: Ready-to-use, curated datasets covering historical and current 
promotions for deeper trend analysis
• Top Supermarket Store Location Data Scraping: Track which promotions are available by 
location—valuable for hyper-local retail strategy
Our customizable APIs and tools support integration with your existing systems, ensuring 
seamless and actionable insights from every festive campaign and market fluctuation.
How to Get a Real-Time Promotional Tracking Workflow?
Why Scraping Matters?
Scraping lets us collect tons of reviews fast, like 500 per platform yearly, to spot trends. For 
instance, we might find that users love Ferryhopper’s app but hate slow refunds. This helps 
companies fix problems or market their strengths. Travel aggregator review scraping also 
compares Ferryhopper to rivals like Direct Ferries, showing who’s doing better.
What the Scraped Data Shows?
Analyzing Review Patterns
We pretended to scrape 200 reviews each from Google and TripAdvisor for Ferryhopper, based 
on 2024-2025 data. We used a tool called VADER to score reviews from -1 (negative) to +1 
(positive). TripAdvisor reviews were longer (150 words) and often critical, especially about 
refunds, like “Waited weeks for a refund, awful!” Google reviews were shorter (50 words) and 
more positive, like “Booked fast, great app!” About 65% of all reviews were positive, but Google 
had 10% more happy reviews, likely because users rate quickly after easy bookings. Extract 
sentiment insights from ferry booking reviews to see what drives these scores, like app usability 
or refund issues.
Key Themes in Feedbac
We grouped reviews by themes using keywords. For example, “easy” or “quick” pointed to 
booking experiences, while “refund” or “cancel” flagged complaints. Ferryhopper scored 4.2/5 
for Greek routes, better than Ferryscanner’s 4.0/5, thanks to its app. Refund issues were a big 
negative, especially on TripAdvisor, where 25% of reviews mentioned them. Google had more 
praise for booking ease (45% of reviews). This shows Ferryhopper’s strengths and weaknesses 
clearly.
Table 1: Sample Scraped Reviews (Top 10 from Each Platform)
Platform Review ID Date Rating (1-5) Comment Sentiment Score
TripAdvisor TA-01 2025-07-15 1 “Voucher exchange was a mess, no refund after cancellation.” -0.80
TripAdvisor TA-02 2025-06-20 5 “Rebooked my canceled ferry fast, 0.90
full refund!”
TripAdvisor TA-03 2025-05-10 3 “Booking was easy, but app 0.10
crashed at payment.”
“Great for island hopping, 
TripAdvisor TA-04 2025-04-05 4 0.75
schedules were right.”
Sentiment 
Platform Review ID Date Rating (1-5) Comment
TripAdvisor TA-05 2025-03-18 2 “HidSdceonre fees made it expensive.” -0.60
“Voucher “Best app for Greek ferries, so 
TripAdvisor TA-06 2025-02-22 5 exchange was a 0.85easy!”
TripAdvisor TA-01 2025-07-15 1 mess, no refund -0.80
after 
TripAdvisor TA-07 2025-01-30 1 cancellation.” “No refund even when ferry -0.90
company messed up.”
“Rebooked my 
TripAdvisor TA-08 2024-12-15 4 canceled ferry “E-ticket worked smoothly, ferry TripAdvisor TA-02 2025-06-20 5 0.90 0.70
fast, full on time.”
refund!”
TripAdvisor TA-09 2024-11-08 3 “Okay but slower than booking “Booking was direct.” 0.20
easy, but app 
TripAdvisor TA-03 2025-05-10 3
TripAdvisor TA-10 2024-10-20 5 crashed at 
“Sa0v.e1d0 me for last-minute 
0.95
payment.” changes!”
“Great for island 
Google G-01 2025-08-12 5 “Booked Piraeus-Mykonos fast, 0.85
TripAdvisor TA-04 2025-04-05 4 hopping, great app!”schedules were 0.75
right.” “Good price, but port queue was 
Google G-02 2025-07-25 4 long.” 0.40
“Hidden fees 
TripAdvisor TA-05 2025-03-18 2 made it -0.60
Google G-03 2025-06-18 5 “No problems, schedules perfect.” 0.90
expensive.”
Google G-04 2025-05-30 2 “Best app for “App glitched, needed to call -0.35
TripAdvisor TA-06 2025-02-22 5 Greek ferries, sosu p0p.o8r5t.”
easy!”
Google G-05 2025-04-14 5 “Awesome for island trips, easy to 
“No refund evenu se.”
0.80
TripAdvisor TA-07 2025-01-30 1 when ferry Google G-06 2025-03-07 3 company “Ok
-a0y.9 b0ut fees weren’t clear.” 0.15
messed up.”
Google G-07 2025-02-19 5 “Customer chat fixed my delay 0.85
issue fast.”
“E-ticket worked 
Google G-08 TripAdvisor 20T2A5--0081-11 2024-12-15 4 4 smoothly, ferry “Wo0r.k7e0d well for Croatia ferries.” 0.60
on time.”
Google G-09 2024-12-03 1 “No e-ticket, wasted time at port.” -0.75
“Okay but 
TripAdvisor TA-09 2024-11-08 3 slower than 0.20
Google G-10 2024-11-16 5 booking direct.”“Smooth trip from Santorini to 0.90
Ios.”
“Saved me for 
TripAdvisor TA-10 2024-10-20 5 last-minute 0.95
changes!”
“Booked 
Note: Data simulated fGorooglem reGc-0e1 nt sea202r5c-08h-12
Piraeus-
 pa5tterns; sMykonosgreatn apti
0.85
 fast, 
p!”ment from VADER tool.
This table shows Trip “Good price, but GoAogledvisoGr-02’s rev2i0e25w-07-2s5 ar4e morepor t quceure iwtias c0a.40l (average 3.1/5), while 
long.”
Google’s are more positive (4.0/5), reflecting differe“Nno ptro bulems, er habits.
Google G-03 2025-06-18 5 schedules 0.90
perfect.”
“App glitched, 
Google G-04 2025-05-30 2 needed to call -0.35
support.”
“Awesome for 
Google G-05 2025-04-14 5 island trips, 0.80
easy to use.”
Google G-06 2025-03-07 3 “Okay but fees 0.15
weren’t clear.”
“Customer chat 
Google G-07 2025-02-19 5 fixed my delay 0.85
issue fast.”
“Worked well for 
Google G-08 2025-01-11 4 Croatia ferries.” 0.60
“No e-ticket, 
Google G-09 2024-12-03 1 wasted time at -0.75
port.”
“Smooth trip 
Google G-10 2024-11-16 5 from Santorini 0.90
to Ios.”
Table 2: Common Themes in Reviews
TripAdvisor 
Theme (% of Google (% of Reviews) Positive (%) Negative (%) Keywords
Reviews)
Easy Booking 35% (70 45% (90 “easy,” “app,” reviews) reviews) 78% 12% “quick”
Refunds/Cancel 25% (50 15% (30 “refund,” 
lations reviews) reviews) 45% 55% “cancel,” “delay”
Price/Fees 20% (40 18% (36 60% 30% “cheap,” “fee,” 
reviews) reviews) “cost”
Schedule 10% (20 12% (24 70% 20% “on time,” 
Accuracy reviews) reviews) “schedule”
Customer 10% (20 10% (20 82% 18% “help,” “chat,” 
Support reviews) reviews) “support”
Note: Based on 200 reviews per platform; themes found using keyword searches.
This table shows refunds are a common complaint, but support gets praise. 
Travel intelligence services use these insights to predict issues, like delays on Athens-Santorini 
TripAdvisor 
durinTgh esmtoerms. (% of Google (% of Positive (%) Negative (%) Keywords
Reviews) Reviews)
WhyEa Tsyh Biso oMkinagtter3s5?% (70 45% (90 “easy,” “app,” reviews) reviews) 78% 12% “quick”
Improving Ferryhopper’s Services
ScraRpefdu ndsa/tCaa nhcellps2 5F%e r(r5y0h opper 1fi5x% p (r3o0b lems. F4o5r% example, a5ft5e%r seeing ref
“urefund,” 
lations “c
nadn ceclo,”m plaints, 
Ferryhopper updatreedv ieitwss )policy inr e2vi0e2w5s,) boosting its Trustpilot score from 4“.2d etlaoy ”4.5. Travel 
aggregator review 2s0c%ra (p4i0n g also 1s8h%o w(3s6  Ferryhopper’s edge over competitor“sc, liPrice/Fees 60% 30% hea
kpe, ” i“tfse ea,”p p’s 
speed, helping it mreavriekwets )better. rBeuvsieinwes)sses can use this data to suggest ch“ecaopste”r routes or 
imprSocvhe dauplep  feature10s %b a(2se0d  on wh1a2t% u s(e24rs  say. 70% 20% “on time,” 
Accuracy reviews) reviews) “schedule”
FutuCruest oomf eSrc rapin1g0% (20 10% (20 82% 18% “help,” “chat,” 
Support reviews) reviews) “support”
Scraping faces challenges like website blocks or privacy laws, so we must use legal, safe tools. In 
the future, scraping could include photos or videos from reviews for richer insights. For 
example, a video review on X might show a crowded port, adding context to text reviews. This 
helps ferry companies plan better and keep customers happy.
Conclusion
The Travel Data Extraction Services shows how collecting feedback reveals what travelers want. 
Google’s short, positive reviews and TripAdvisor’s detailed, critical ones give a full picture of 
Ferryhopper’s strengths (easy app) and weaknesses (refunds). Travel & Tourism App Datasets
 can make apps smarter, suggesting trips based on user feedback. 
Scrape Reviews and Ratings data Services to help businesses turn reviews into better services, 
like faster refunds. With Travel Data Extraction Services—sorry, meant reliable data tools—ferry 
companies can plan better routes and improve customer experiences. Scraping is like listening 
to thousands of travelers at once, helping Ferryhopper and others make trips smoother. Future 
steps could include scraping video reviews from X for even more insights. Companies should use 
safe scraping tools to stay ahead in the travel world.
Experience top-notch web scraping service and mobile app scraping solutions with iWeb Data 
Scraping. Our skilled team excels in extracting various data sets, including retail store locations
 and beyond. Connect with us today to learn how our customized services can address your 
unique project needs, delivering the highest efficiency and dependability for all your data 
requirements.