Data as of Oct 13, 2026Based on 178 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 | 37% |
| 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% |
| 4 | structured sentiment monitoring and trend forecasting across broad social channels | structured sentiment monitoring and trend forecasting across broad social channels | 16% |
| 6 | multilingual social listening detecting slang, irony, and early conversational shifts | multilingual social listening detecting slang, irony, and early conversational shifts | 15% |
| 7 | affective computing models capturing emotional nuance across text and audio | affective computing models capturing emotional nuance across text and audio | 14% |
| 7 | public sector social listening focused on community feedback and needs | public sector social listening focused on community feedback and needs | 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 | 11% | ||
| 11 | 11% | ||
| 11 | 11% | ||
| 14 | 10% | ||
| 14 | 10% |
Weeks of Sep 21 – Oct 11, 2026
Widest split: Brandwatch, 28% on ChatGPT Search and 45% on Google AI Mode.
| Brand | ChatGPT Search | Google AI Mode | Comparison |
|---|---|---|---|
| Brandwatch | 28% | 45% | |
| Meltwater | 19% | 29% | |
| Zencity | 17% | 24% | |
| Sprout Social | Outside ChatGPT Search's top 25 | 28% |
34% 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.
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?
Market map
Mentioned in · since Jul 6
Leading brands by topic