How Sentiment Search Uses Web Scraping for Sentiment Analysis Across Thousands of Restaurant Reviews

About Sentiment Search

Sentiment Search is a customer feedback analytics company built for the hospitality industry, founded by Prithvi Dhanda. Its platform rests on two core technologies, natural language processing and sentiment analysis, aggregating reviews for thousands of restaurants across many cities and turning that raw feedback into comprehensive insights and search capabilities a brand can act on. Today it serves more than 9,000 customers across 21 countries, with a hospitality client list that includes Guinness, Hawksmoor, Franco Manca, Popeyes, and Prezzo.

Challenge

A sentiment engine is only as good as the text you feed it. The hard part was never the NLP. It was getting clean, complete data from every source where diners actually talk:

Data before algorithms:

Before any model can decide whether a comment is positive, negative, or sarcastic, that comment has to be found, pulled off a review site or social feed, and delivered in structured form.

A crawler for every site:

The platform needs specific content from specific pages, which means building and maintaining a separate crawler for each source it wants to cover.

Sources with no API:

Many review and social platforms offer no API at all, so the only way in is to scrape pages that each have their own structure and change it without warning.

Fragility at scale:

A layout change on one review site silently breaks a crawler and leaves gaps, and a missed wave of negative reviews is exactly the failure a restaurant client pays to avoid, multiplied across thousands of restaurants and cities.

For a platform serving this many venues, source collection was not a background task. It was the thing that quietly decided whether the whole product worked.

Why PromptCloud

Prithvi Dhanda found PromptCloud while searching for data crawling companies online. The fit came down to four things:

A comprehensive setup:

A managed service that made collecting and processing data relatively easy, rather than a stack of scripts to babysit.

Coverage of no-API sources:

Reliable access to the exact sites that are hardest to collect and easiest to lose, the ones with no API of their own.

Maintenance absorbed:

When a source changes its layout, absorbing that change becomes PromptCloud’s job, not a gap in a client’s dashboard.

Reliable support:

Support that stayed dependable as the platform’s needs changed and grew.

PromptCloud’s fully managed data crawling service handles the sources that Sentiment Search’s engines depend on, delivering clean, structured data on a set schedule. That moved source reliability off the founder’s plate and let the team focus on the part only it could do: reading emotion in text accurately.

Implementation

Where PromptCloud sits in the pipeline

PromptCloud runs the source collection layer beneath Sentiment Search’s sentiment models:

Source data acquisition:

Structured data from review platforms, social networks, and delivery apps, including the sources that expose no API.

A uniform daily feed:

Data arrives in a consistent shape that the sentiment engines can read the same way every day, so scoring stays comparable across sources.

Layout-change monitoring:

When a review site redesigns its pages, PromptCloud absorbs it in the background so coverage does not gap.

Results

With source collection handled by a managed pipeline, Sentiment Search’s own team could spend its time differently:

Consistent daily coverage:

Reviews and social posts flowing across many restaurants and cities, instead of gaps wherever an in-house crawler had broken.

Reliable access to no-API sites:

Dependable collection from the exact sources that are hardest to reach and easiest to lose.

Comparable scoring:

Structured, uniform data feeding the models, so sentiment stays consistent across sources rather than shifting with each site’s format.

Engineering focused on the product:

Attention spent on NLP and sentiment accuracy rather than on repairing crawlers.

A foundation for growth:

A data layer stable enough to support the platform’s growth into a system now serving 9,000+ customers across 21 countries for major hospitality brands.

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