An enterprise social listening platform needed a social listening API to track more than 5,000 keywords and phrases across specific European countries, queryable through simple boolean searches.
Client Overview
An enterprise social listening platform needed to run analysis on specific tweets matching a defined set of keywords and phrases, tracking conversations relevant to its own clients across specific European countries. Running this kind of monitoring at scale meant handling a constantly shifting stream of tweets rather than a fixed dataset that could be pulled once and analyzed later.
The platform’s core competency was analysis, not the infrastructure needed to crawl and filter a live stream of social data at this volume. Building and maintaining that crawling infrastructure in house would have pulled focus away from what the platform actually did for its own enterprise clients, so it needed a facilitator to handle the data collection side of a social listening API entirely.
Client Requirements
The platform’s brief to PromptCloud covered scope, geography, and query flexibility:
- Monitoring for a list of more than 5,000 keywords and phrases, editable at any time
- Tweets collected only from specific countries in Europe
- A continuous feed of matching tweets as they appeared, not a periodic batch
- Boolean query support, combining keywords with AND, OR, and NOT
- Results delivered in JSON format through a queryable API
Challenges
Monitoring more than 5,000 keywords and phrases against a live stream of tweets is fundamentally different from matching against a static dataset, the kind of problem a social listening API has to solve at scale. Twitter’s own volume meant the set of tweets to check against those 5,000 terms was constantly growing, and doing this reliably meant the matching logic itself had to keep pace in real time, not catch up periodically.
Geography added another layer. Restricting collection to specific European countries meant every tweet needed accurate location context before it could even be checked against the keyword list, and getting that geographic filtering wrong would have meant either missing relevant conversations or including ones from entirely the wrong market, undermining the whole point of the platform’s regional analysis.
Solutions
PromptCloud built this around geo-location tagging, keyword matching against the client’s list, and a queryable API, so the platform’s own team only ever touched finished results.
Geo-Tagged Monitoring Across Specific Countries
PromptCloud used geo-location tagging capabilities to restrict tweet collection to the specific European countries the platform cared about, rather than collecting broadly and filtering by location afterward. Building that restriction into the collection stage itself meant the social listening API delivered only conversations relevant to the platform’s actual target markets, not a wider set of tweets that then needed further filtering on the platform’s own side. That distinction mattered for a client whose entire value proposition depended on relevance to specific regional clients.
Matching Against a List of 5,000 Keywords
Every tweet passing through the geo-tagged stream was checked against the client’s list of more than 5,000 keywords and phrases, with any match flowing into the delivered feed. Since the list could be modified at any time, the platform’s own team could add, remove, or adjust terms as its enterprise clients’ needs shifted, without needing a new project each time. This kind of dynamic, high-volume keyword matching is worth comparing across different providers, including how PromptCloud’s approach stacks up against other social data providers built for smaller or more static keyword sets.
Boolean Queries Through a Single API
Extracted data was indexed on PromptCloud’s API so the platform could query it using combinations of AND, OR, and NOT, a single line query like a brand name paired with a topic, or a brand name paired with a term to exclude. That flexibility meant the platform’s own analysts could ask precise questions directly against the data rather than exporting a broad feed and filtering it themselves after the fact.
Results Delivered Ready to Use
Query results came back in JSON format, ready for the platform to consume directly in its own systems without an extra parsing or cleanup step. Combined with a continuous feed of matching tweets as they appeared, this meant the platform’s team worked entirely with finished, structured results, never with the raw mechanics of collecting and filtering a live social media stream.
Social Listening Data Pipeline, Before and After PromptCloud
| Area | Before | After |
| Geographic filtering | Would require filtering after broad collection | Built into collection via geo-location tagging |
| Keyword matching | Static list assumed | 5,000+ keywords, modifiable at any time |
| Querying | Would need custom filtering logic | Boolean AND, OR, NOT queries via API |
| Data format | Not specified, likely needing processing | Structured JSON, ready to consume |
Benefits to the Client
The platform got a continuous feed of relevant tweets as they appeared, matched against a keyword list of more than 5,000 terms that could be changed at any time without a new project. Tweets arrived already restricted to the specific European countries that mattered, so the platform’s own team never had to filter out conversations from the wrong region.
Single line boolean queries meant analysts could ask precise questions, a brand paired with a topic, or a brand paired with an excluded term, directly against the data. Ready made, structured results in JSON format meant the platform’s core team stayed focused on analysis, the thing its enterprise clients actually paid for, rather than on the infrastructure behind collecting and filtering a live social stream.
A Social Listening API Built for Precision, Not Just Volume
Monitoring 5,000-plus keywords across a live stream of tweets is not worth much if the results are not restricted to the right geography or queryable with real precision. A social listening API only earns its place in an enterprise platform’s stack if analysts can ask exact questions and trust the answer reflects the right market.
That is what geo-tagged collection, a flexible keyword list, and boolean querying through one API actually deliver, relevant results by design rather than a broad feed the platform has to narrow down itself after the fact.
