Data as of Oct 18, 2026Based on 468 AI responses
Reviewed by Dimitry Apollonsky
AI summary
Chattermill and Enterpret are nearly tied as recommendations for customer experience feedback analytics and multi-channel sentiment tracking. When buyers need to summarize chats directly into product feedback themes, Enterpret is the usual answer.
Rank and Mentioned in · since Jul 5
| # | Brand | Recommended for | Mentioned in |
|---|---|---|---|
| 1 | unifying cross-channel feedback to map sentiment against business metrics | unifying cross-channel feedback to map sentiment against business metrics | 37% |
| 2 | product teams grouping multi-channel feedback into product impact themes | product teams grouping multi-channel feedback into product impact themes | 35% |
| 3 | automated discovery of themes and aspect-based sentiment without manual dictionaries | automated discovery of themes and aspect-based sentiment without manual dictionaries | 34% |
| 4 | general sentiment analysis and entity extraction from financial news | general sentiment analysis and entity extraction from financial news | 29% |
| 5 | automated root-cause tagging across support tickets and chat transcripts | automated root-cause tagging across support tickets and chat transcripts | 29% |
| 6 | high-volume opinion mining and aspect-based sentiment across multiple languages | high-volume opinion mining and aspect-based sentiment across multiple languages | 25% |
| 7 | targeted sentiment analysis and handling industry-specific jargon through custom classification | targeted sentiment analysis and handling industry-specific jargon through custom classification | 22% |
| 8 | granular sentence-level sentiment scoring within customer experience management platforms | granular sentence-level sentiment scoring within customer experience management platforms | 14% |
| 9 | scalable cloud NLP infrastructure for targeted feedback analysis | scalable cloud NLP infrastructure for targeted feedback analysis | 13% |
| 10 | detecting subtle context, sarcasm, and nuanced customer intents | detecting subtle context, sarcasm, and nuanced customer intents | 13% |
| 11 | 13% | ||
| 12 | 10% | ||
| 13 | 9% | ||
| 14 | 9% | ||
| 15 | 9% |
What’s changing · since July
Since July, SentiSumrising has become the most named option for AI ticket classification, while Qualtricsrising has taken the lead for feedback tool integrations. Enterpretrising and Thematicrising have also become common answers across customer feedback and aspect-based sentiment questions.
Week of Aug 31–Sep 6 · Since Jul 5
Widest split: Enterpret, 25% on ChatGPT Search and 46% on Google AI Mode.
| Brand | ChatGPT Search | Google AI Mode | Comparison |
|---|---|---|---|
| Chattermill | 33% | 41% | |
| Enterpret | 25% | 46% | |
| Thematic | 30% | 39% | |
| Google Cloud Natural Language | 33% | 25% | |
| SentiSum | 20% | 37% | |
| Azure AI Language | 30% | 19% | |
| Amazon Comprehend | 31% | 13% | |
| Qualtrics | 14% | 14% | |
| AWS | 15% | 12% | |
| OpenAI | 10% | 16% |
50% of citations point at the ranked brands’ own sites. youtube.com, arxiv.org, getthematic.com are the most-cited sites outside the ranked brands’ own domains.
Market map
Mentioned in · since Jul 5
Leading brands by topic
I need a summarization tool that extracts specific entities (names/dates) and does not just give a general abstract.
What's a good API for sentiment analysis that can handle industry-specific jargon and sarcasm well?
I am looking for a text analysis API that can extract entities and sentiment from financial news.
What’s the best system for summarizing chats into product feedback themes?
I want a sentiment analyzer that detects intent (to buy/churn) and does not just classify positive/negative.
We use a customer feedback tool. Who offers a text analytics integration to analyze sentiment?
I need a sentiment analyzer that detects sarcasm and does not just look for positive keywords.
Which AI platforms are best for classifying support tickets by product, feature, and sentiment analysis?
What aspect-based sentiment analysis APIs can handle high-scale user feedback categorization?
Are there any APIs capable of aspect-based sentiment analysis that can handle high-volume user feedback categorization?