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GeoIp2\Model\City Object
(
    [raw:protected] => Array
        (
            [city] => Array
                (
                    [geoname_id] => 4509177
                    [names] => Array
                        (
                            [de] => Columbus
                            [en] => Columbus
                            [es] => Columbus
                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
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                            [fr] => Amérique du Nord
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                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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                    [iso_code] => US
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                        (
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                            [ru] => США
                            [zh-CN] => 美国
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            [location] => Array
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                    [longitude] => -83.0061
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            [postal] => Array
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                    [code] => 43215
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            [registered_country] => Array
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                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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            [subdivisions] => Array
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                                    [es] => Ohio
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                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
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                        )

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            [traits] => Array
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                    [ip_address] => 216.73.216.150
                    [prefix_len] => 22
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        )

    [continent:protected] => GeoIp2\Record\Continent Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [code] => NA
                    [geoname_id] => 6255149
                    [names] => Array
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                            [de] => Nordamerika
                            [en] => North America
                            [es] => Norteamérica
                            [fr] => Amérique du Nord
                            [ja] => 北アメリカ
                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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                )

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            [validAttributes:protected] => Array
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                    [0] => code
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    [country:protected] => GeoIp2\Record\Country Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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            [validAttributes:protected] => Array
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                    [0] => confidence
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                    [3] => isoCode
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    [locales:protected] => Array
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    [maxmind:protected] => GeoIp2\Record\MaxMind Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                )

            [validAttributes:protected] => Array
                (
                    [0] => queriesRemaining
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        )

    [registeredCountry:protected] => GeoIp2\Record\Country Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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        )

    [traits:protected] => GeoIp2\Record\Traits Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [ip_address] => 216.73.216.150
                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
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            [validAttributes:protected] => Array
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                    [0] => autonomousSystemNumber
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                    [2] => connectionType
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                    [8] => isHostingProvider
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                    [13] => isSatelliteProvider
                    [14] => isTorExitNode
                    [15] => mobileCountryCode
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                    [19] => staticIpScore
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                )

        )

    [city:protected] => GeoIp2\Record\City Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [geoname_id] => 4509177
                    [names] => Array
                        (
                            [de] => Columbus
                            [en] => Columbus
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                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
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    [location:protected] => GeoIp2\Record\Location Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
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                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
                )

            [validAttributes:protected] => Array
                (
                    [0] => averageIncome
                    [1] => accuracyRadius
                    [2] => latitude
                    [3] => longitude
                    [4] => metroCode
                    [5] => populationDensity
                    [6] => postalCode
                    [7] => postalConfidence
                    [8] => timeZone
                )

        )

    [postal:protected] => GeoIp2\Record\Postal Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [code] => 43215
                )

            [validAttributes:protected] => Array
                (
                    [0] => code
                    [1] => confidence
                )

        )

    [subdivisions:protected] => Array
        (
            [0] => GeoIp2\Record\Subdivision Object
                (
                    [record:GeoIp2\Record\AbstractRecord:private] => Array
                        (
                            [geoname_id] => 5165418
                            [iso_code] => OH
                            [names] => Array
                                (
                                    [de] => Ohio
                                    [en] => Ohio
                                    [es] => Ohio
                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
                                )

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                    [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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                    [validAttributes:protected] => Array
                        (
                            [0] => confidence
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)
 country : United States
 city : Columbus
US
Array
(
    [as_domain] => amazon.com
    [as_name] => Amazon.com, Inc.
    [asn] => AS16509
    [continent] => North America
    [continent_code] => NA
    [country] => United States
    [country_code] => US
)

Introduction

In the competitive hospitality industry, understanding guest experiences is critical to improving service quality, building brand loyalty, and driving repeat bookings. Today, hotels and travel platforms generate thousands of online reviews daily, particularly on Google and TripAdvisor, reflecting real-time guest sentiment. To convert this vast unstructured data into actionable insights, businesses increasingly rely on advanced analytics tools to Scrape Sentiment Trends for Google & TripAdvisor Reviews.

This research report examines sentiment patterns across 20,000+ hospitality reviews collected from 2020 to 2026. The analysis focuses on positivity and negativity trends, service gaps, and category-specific feedback such as cleanliness, staff behavior, amenities, and food quality. With insights drawn from structured data pipelines and automated review extraction, hospitality brands can proactively address operational inefficiencies, refine service offerings, and enhance overall guest satisfaction.

Understanding Guest Feedback Dynamics

Between 2020 and 2026, online reviews have become a critical barometer of guest satisfaction. Hotels in North America and Europe, for example, received a surge in feedback post-pandemic as travel resumed, while Southeast Asia saw growing review volumes due to tourism recovery. Using tools that Extract Google & TripAdvisor Reviews for Hospitality Brands, businesses can quantify sentiment and track trends over time.

Year Total Reviews Analyzed Positive (%) Neutral (%) Negative (%)
2020 12,500 65 20 15
2021 14,200 68 18 14
2022 16,800 70 17 13
2023 18,400 72 16 12
2024 19,500 73 15 12
2025 20,000 74 14 12
2026* 21,000 75 13 12

*Projected

Analysis reveals increasing positivity in reviews, particularly related to digital check-in, contactless services, and loyalty program benefits. Negative feedback primarily highlights service gaps such as delayed housekeeping or inconsistent food quality. Structured extraction of review data allows hospitality managers to focus on key improvement areas, reducing repeated complaints and elevating the guest experience.

Tracking Service Performance in Real-Time

The modern hospitality landscape demands instant insights. With Real-time Hospitality Review Monitoring via Scraping, hotels can track sentiment as it happens, identifying issues before they escalate. This real-time approach has been essential from 2020 onward, especially during peak travel seasons.

Metric 2020 2022 2024 2026*
Avg. Reviews per Day 45 65 85 100
Avg. Negative Reviews Detected 7 9 11 12
Avg. Response Time (hrs) 48 36 24 18

*Projected

Real-time monitoring not only helps address complaints faster but also enables trend analysis across service categories, such as housekeeping, food & beverage, and front-desk operations. Hotels using this approach report higher guest satisfaction scores and improved online reputation management.

Extracting Platform-Specific Insights

TripAdvisor remains a key source of structured hospitality feedback. TripAdvisor Review Data Extraction tools allow brands to analyze category-specific sentiment, providing granular insights into aspects like room quality, location convenience, and amenities.

Category Avg. Positive (%) Avg. Negative (%)
Room Cleanliness 78 10
Food & Beverage 70 15
Staff Service 82 8
Facilities & Amenities 75 12

By extracting large volumes of reviews, hospitality managers can benchmark performance across multiple properties and regions. For example, European hotels report higher satisfaction in staff service, while North American properties receive stronger feedback on food and beverage quality. This granular extraction ensures targeted operational improvements.

Unlocking Google Review Insights

Google Reviews provide a comprehensive view of customer sentiment, including ratings, text feedback, and service-specific comments. By leveraging Google Reviews Scraping for Hospitality Brands, hotels can track positivity/negativity trends, identify recurring complaints, and correlate ratings with operational performance.

Year Avg. Rating Avg. Positive Reviews (%) Avg. Negative Reviews (%)
2020 4.1 65 15
2021 4.2 67 14
2022 4.3 70 13
2023 4.4 72 12
2024 4.4 73 12
2025 4.5 74 12
2026* 4.5 75 12

*Projected

Structured Google review data also allows sentiment correlation with pricing, promotions, and seasonal trends. For instance, negative reviews often spike during high-occupancy periods, highlighting the need for proactive staffing and service adjustments.

Measuring and Analyzing Guest Sentiment

Beyond extraction, Customer Review Sentiment Analysis transforms raw text into actionable insights. Using natural language processing (NLP), hospitality brands can identify service gaps, understand category-specific concerns, and detect emerging trends.

Aspect Positive Sentiment (%) Negative Sentiment (%)
Housekeeping 80 12
Front Desk 85 10
Food Quality 70 15
Amenities 75 12

Analyzing sentiment trends over multiple years highlights improvements or persistent gaps. For example, while housekeeping feedback has improved steadily from 2020 to 2026, food quality remains a recurring concern for certain properties. Such insights guide strategic investments, training programs, and operational changes.

Aggregating Data Across Platforms

Hotels and travel brands often consolidate insights from multiple platforms. Hotel Review Aggregation for Travel Platforms allows businesses to combine Google, TripAdvisor, and other online reviews to generate holistic performance dashboards.

Year Avg. Aggregated Rating Positive Feedback (%) Negative Feedback (%)
2020 4.2 67 14
2021 4.3 69 13
2022 4.4 71 12
2023 4.4 72 12
2024 4.5 73 12
2025 4.5 74 12
2026* 4.6 75 12

*Projected

Aggregated insights allow hospitality chains to monitor trends across regions, identify top-performing properties, and detect systemic issues. Data-driven dashboards support real-time decision-making, benchmarking, and resource allocation.

Actowiz Solutions provides cutting-edge solutions to Scrape Sentiment Trends for Google & TripAdvisor Reviews, helping hospitality brands unlock actionable insights from massive volumes of guest feedback. Our platform supports scalable review extraction, real-time monitoring, and advanced sentiment analytics. With high-quality datasets, category-wise sentiment reporting, and customizable dashboards, Actowiz empowers brands to proactively address service gaps, improve guest experiences, and maintain a competitive edge in the hospitality industry.

Conclusion

In today’s hospitality sector, success depends on translating customer feedback into actionable strategies. By leveraging Web Crawling Service, Web Data Mining, and Real-time Dashboards, hotels and travel brands can analyze sentiment trends, detect service gaps, and optimize offerings across multiple platforms.

Partner with Actowiz Solutions today to Scrape Sentiment Trends for Google & TripAdvisor Reviews and transform guest feedback into strategic growth opportunities.

From Raw Data to Real-Time Decisions

All in One Pipeline

Scrape Structure Analyze Visualize

Look Back Analyze historical data to discover patterns, anomalies, and shifts in customer behavior.

Find Insights Use AI to connect data points and uncover market changes. Meanwhile.

Move Forward Predict demand, price shifts, and future opportunities across geographies.

Industry:

Fintech / Digital Payments

Result

Accurate daily voucher &

cashback visibility across platforms

★★★★★

“Actowiz Solutions helped us automate daily voucher and cashback data collection across PhonePe, Paytm, Flipkart, and Hubble. The API-driven delivery significantly improved offer accuracy and operational efficiency.”

Product Manager, Fintech Platform (India)

✓ Daily voucher & cashback tracking via Push & Pull APIs

Industry:

Coffee / Beverage / D2C

Result

2x Faster

Smarter product targeting

★★★★★

“Actowiz Solutions has been instrumental in optimizing our data scraping processes. Their services have provided us with valuable insights into our customer preferences, helping us stay ahead of the competition.”

Operations Manager, Beanly Coffee

✓ Competitive insights from multiple platforms

Industry:

Real Estate

Result

2x Faster

Real-time RERA insights for 20+ states

★★★★★

“Actowiz Solutions provided exceptional RERA Website Data Scraping Solution Service across PAN India, ensuring we received accurate and up-to-date real estate data for our analysis.”

Data Analyst, Aditya Birla Group

✓ Boosted data acquisition speed by 3×

Industry:

Organic Grocery / FMCG

Result

Improved

competitive benchmarking

★★★★★

“With Actowiz Solutions' data scraping, we’ve gained a clear edge in tracking product availability and pricing across various platforms. Their service has been a key to improving our market intelligence.”

Product Manager, 24Mantra Organic

✓ Real-time SKU-level tracking

Industry:

Quick Commerce

Result

2x Faster

Inventory Decisions

★★★★★

“Actowiz Solutions has greatly helped us monitor product availability from top three Quick Commerce brands. Their real-time data and accurate insights have streamlined our inventory management and decision-making process. Highly recommended!”

Aarav Shah, Senior Data Analyst, Mensa Brands

✓ 28% product availability accuracy

✓ Reduced OOS by 34% in 3 weeks

Industry:

Quick Commerce

Result

3x Faster

improvement in operational efficiency

★★★★★

“Actowiz Solutions' data scraping services have helped streamline our processes and improve our operational efficiency. Their expertise has provided us with actionable data to enhance our market positioning.”

Business Development Lead,Organic Tattva

✓ Weekly competitor pricing feeds

Industry:

Beverage / D2C

Result

Faster

Trend Detection

★★★★★

“The data scraping services offered by Actowiz Solutions have been crucial in refining our strategies. They have significantly improved our ability to analyze and respond to market trends quickly.”

Marketing Director, Sleepyowl Coffee

Boosted marketing responsiveness

Industry:

Quick Commerce

Result

Enhanced

stock tracking across SKUs

★★★★★

“Actowiz Solutions provided accurate Product Availability and Ranking Data Collection from 3 Quick Commerce Applications, improving our product visibility and stock management.”

Growth Analyst, TheBakersDozen.in

✓ Improved rank visibility of top products

Trusted by Industry Leaders Worldwide

Real results from real businesses using Actowiz Solutions

★★★★★
'Great value for the money. The expertise you get vs. what you pay makes this a no brainer"
Thomas Gallao
Thomas Galido
Co-Founder / Head of Product at Upright Data Inc.
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2 min
★★★★★
“I strongly recommend Actowiz Solutions for their outstanding web scraping services. Their team delivered impeccable results with a nice price, ensuring data on time.”
Thomas Gallao
Iulen Ibanez
CEO / Datacy.es
Product Image
1 min
★★★★★
“Actowiz Solutions offered exceptional support with transparency and guidance throughout. Anna and Saga made the process easy for a non-technical user like me. Great service, fair pricing highly recommended!”
Thomas Gallao
Febbin Chacko
-Fin, Small Business Owner
Product Image
1 min

See Actowiz in Action – Real-Time Scraping Dashboard + Success Insights

Blinkit (Delhi NCR)

In Stock
₹524

Amazon USA

Price Drop + 12 min
in 6 hrs across Lel.6

Appzon AirPdos Pro

Price
Drop −12 thr

Zepto (Mumbai)

Improved inventory
visibility & planning

Monitor Prices, Availability & Trends -Live Across Regions

Actowiz's real-time scraping dashboard helps you monitor stock levels, delivery times, and price drops across Blinkit, Amazon: Zepto & more.

✔ Scraped Data: Price Insights Top-selling SKUs

Our Data Drives Impact - Real Client Stories

Blinkit | India (Retail Partner)

"Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"

✔ Scraped Data, SKU availability, delivery time

US Electronics Seller (Amazon - Walmart)

With hourly price monitoring, we aligned promotions with competitors, drove 17%

✔ Scraped Data, SKU availability, delivery time

Zepto Q Commerce Brand

"Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"

✔ Scraped Data, SKU availability, delivery time

Actowiz Insights Hub

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How Web Scraping Instacart Product Availability by Zip Code Helps Retailers Optimize Inventory

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