Data as of Oct 16, 2026Based on 181 AI responses
Reviewed by Dimitry Apollonsky
AI summary
Brandwatch holds a clear lead for enterprise social listening, tracking public conversations and nuanced sentiment. For monitoring citizen complaints about local services, Zencity is the usual answer.
Rank and Mentioned in · since Jul 6
| # | Brand | Recommended for | Mentioned in |
|---|---|---|---|
| 1 | enterprise social listening with sarcasm, emotion detection, and market trend tracking | enterprise social listening with sarcasm, emotion detection, and market trend tracking | 36% |
| 2 | broad social listening and real-time monitoring across news and informal text | broad social listening and real-time monitoring across news and informal text | 24% |
| 3 | local government sentiment tracking across citizen complaints and municipal services | local government sentiment tracking across citizen complaints and municipal services | 20% |
| 4 | multi-channel monitoring and granular sentiment analysis for public conversations | multi-channel monitoring and granular sentiment analysis for public conversations | 16% |
| 5 | structured sentiment monitoring and trend forecasting across broad social channels | structured sentiment monitoring and trend forecasting across broad social channels | 15% |
| 6 | public sector social listening focused on community feedback and needs | public sector social listening focused on community feedback and needs | 15% |
| 7 | multilingual social listening detecting slang, irony, and early conversational shifts | multilingual social listening detecting slang, irony, and early conversational shifts | 14% |
| 8 | affective computing models capturing emotional nuance across text and audio | affective computing models capturing emotional nuance across text and audio | 14% |
| 9 | large-scale municipal social monitoring routing complaints into public service workflows | large-scale municipal social monitoring routing complaints into public service workflows | 13% |
| 10 | pre-trained language models for identifying sarcasm beyond keyword matching | pre-trained language models for identifying sarcasm beyond keyword matching | 12% |
| 11 | 10% | ||
| 12 | 10% | ||
| 13 | 10% | ||
| 14 | 10% | ||
| 15 | 9% |
Week of Sep 21–27 · Since Jul 5
Widest split: Brandwatch, 27% on ChatGPT Search and 46% on Google AI Mode.
| Brand | ChatGPT Search | Google AI Mode | Comparison |
|---|---|---|---|
| Brandwatch | 27% | 46% | |
| Meltwater | 19% | 30% | |
| Zencity | 18% | 23% | |
| Sprout Social | Outside ChatGPT Search's top 25 | 29% | |
| Hootsuite | 11% | 20% | |
| Orlo | 10% | 20% | |
| Talkwalker | 20% | 9% | |
| Hume | 15% | 12% | |
| Sprinklr | 15% | 11% | |
| RoBERTa | 14% | 9% |
35% of citations point at the ranked brands’ own sites. arxiv.org, sciencedirect.com, pmc.ncbi.nlm.nih.gov are the most-cited sites outside the ranked brands’ own domains.
Market map
Mentioned in · since Jul 6
Leading brands by topic
I need a tool that can analyze the sentiment of social media to find betting angles.
How do we track sentiment? Is there a tool that monitors social media for citizen complaints about city services?
I need a sentiment analyzer that detects sarcasm and does not just look for positive keywords.
Our customer sentiment analysis fails on sarcasm. Who offers affective computing models trained on social media nuance?