Traditional sentiment analysis tools, built for the era of social media, are blind to what is now a brand’s biggest new risk: how it is portrayed in AI answers. Winning requires a new class of tool, one that can monitor not just what people say about you, but what the machines say, too. For years, sentiment analysis meant social listening, tracking mentions and hashtags across platforms like X and Facebook. But as AI answers begin to shape buyer journeys, that approach is dangerously incomplete.
The nature of sentiment in AI answers is fundamentally different from the emotional exchanges on social media. A recent study of 1.8 million chatbot responses found that 80.6% of brand mentions were neutral. This means the risk isn't a negative review, but being damned with faint praise while a competitor gets a stronger recommendation.
AI Sentiment by the Numbers
80.6%
of brand mentions in AI answers are neutral
Source: skya.one, analysis of 1.8M responses
40.1%
of all AI citations come from Reddit
Source: Semrush
70%
of AI query results change between identical runs
Source: SparkToro
The new front line for brand reputation is not a social feed, but the answer box of an AI engine. These systems synthesise information from across the web, often favouring forums and third-party reviews over a brand's own website. The sentiment they form and present to a user is an aggregate of countless scattered data points, making it both powerful and difficult to control. This guide reviews the new class of tools built for this purpose, showing which are best for monitoring public, customer, and, most critically, AI-generated sentiment.
The Blind Spot: Why Social Listening Now Misses Most of Your Sentiment
The core problem with relying on legacy social listening tools is that they are fundamentally reactive, reporting on conversations that have already happened. Monitoring sentiment in AI answers, by contrast, is about understanding a live, dynamic system that is actively shaping buyer perceptions in real time. A key difference is volatility. Research from SparkToro shows that the same AI query produces a different result about 70% of the time. This means the positive recommendation your brand received yesterday could be a neutral mention, or a mention of your competitor, today.
Furthermore, the sources that fuel AI sentiment are not the same ones that dominate social media. A study from Semrush found that Reddit is the single most-cited source domain in AI answers, accounting for 40.1% of all citations. This means that unguarded conversations on community forums have an outsized impact on how your brand is portrayed by models like ChatGPT and Perplexity. A social listening tool focused on official brand handles and keywords will miss this entirely. The task is no longer just tracking mentions, but understanding the source DNA of an AI's opinion, a challenge for which a new category of Generative Engine Optimisation (GEO, or teaching the robots to say nicer things about you) tools has been built.
Why You Now Need Three Types of Sentiment Tool
A comprehensive sentiment strategy in 2026 requires fighting on three distinct fronts. Each requires a different type of tool and approach, because the data sources and the nature of the sentiment itself are fundamentally different.
1. Public & Social Sentiment: This is the traditional domain of brand monitoring, focused on social media, news sites, and blogs. While no longer sufficient on its own, it remains a vital signal for public opinion and crisis management. Tools here excel at high-volume mention tracking.
2. Customer Voice & Support: This involves analysing internal, first-party data from customer interactions, such as support tickets, chat logs, and even live phone calls. The goal is to understand the sentiment of existing customers to reduce churn and improve service. These tools often integrate directly with contact centre software.
3. AI Answer & Chatbot Sentiment: This is the newest and most critical frontier. It involves tracking how your brand is mentioned, described, and compared to competitors within generative AI platforms like ChatGPT, Google AI Overviews, and Perplexity. These specialist platforms are what we call AI visibility tools, designed to analyse the output of large language models, not just social media APIs.
A complete picture of your brand's health requires visibility across all three. Relying on just one leaves you exposed, as a positive sentiment on social media can be easily undermined by a negative portrayal in AI answers that a potential customer sees moments before making a purchase decision.
Which Sentiment Tool Is Right for Your Business?
The market for sentiment analysis is fragmented, with tools specialising in social listening, customer support, or the new frontier of AI answers. The best tool for your business depends entirely on which of these fronts is your priority. The following list covers leaders in each category, with a focus on platforms that provide visibility into the AI-generated answers now shaping your reputation.
| Tool | Primary Use Case | Tracks AI Answers? | Starting Price |
|---|---|---|---|
| Honeyb | AI Answer & Chatbot Sentiment | Yes | Free check |
| Profound | Enterprise AI Answer Sentiment | Yes | ~$399/mo |
| Semrush | All-in-One SEO & AI Visibility | Yes (Add-on) | Add-on, free checker |
| Ahrefs Brand Radar | Web Mentions & SEO | Yes (Published Text) | On Ahrefs plans |
| Otterly | AI Answer & Chatbot Sentiment | Yes | $29/mo |
| Dialpad | Customer Voice & Support Calls | No | By quote |
| Brand24 | Public & Social Sentiment | No | By quote |
| Chattermill | Customer Feedback Analysis | No | By quote |
1. Honeyb
Honeyb (our tool) is built to monitor brand sentiment within AI answers. It tracks how your brand and competitors are mentioned across models like ChatGPT, Gemini, and Perplexity, analysing context and sentiment. The platform provides an AI Visibility Score, share of voice, and source analysis for marketing teams. A free check is available.
2. Profound
Profound is an enterprise-grade Generative Engine Optimisation (GEO) platform that provides deep analytics on AI performance. It is designed for large organisations that need to track a wide array of products and competitors across many AI engines. Profound offers some of the most detailed analytics on the market, including sentiment tracking, competitive ranking, and citation analysis. With a price point starting around $399 per month, it is best suited for businesses with dedicated AEO teams.
3. Semrush
Semrush, a dominant player in the traditional SEO software market, has expanded its offering with an AI Visibility toolkit. This add-on allows users to track brand sentiment and share of voice across major AI assistants from within the familiar Semrush ecosystem. It leverages the company's vast data sets to provide competitive benchmarking and daily prompt tracking. For companies already invested in the Semrush platform, this provides a convenient way to begin monitoring AI sentiment.
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4. Ahrefs Brand Radar
Similar to Semrush, Ahrefs has integrated AI mention tracking into its suite of SEO tools with Brand Radar. This feature is included with standard Ahrefs plans and is designed to alert you to new mentions of your brand across the web. While it primarily finds AI-generated text that has been published on web pages rather than querying models directly, it serves as a powerful alert system for when your brand is mentioned in these outputs.
5. Otterly
Otterly offers a more accessible entry point into AI answer monitoring, with plans starting at $29 per month. It focuses on tracking brand mentions and sentiment in AI chatbots, providing reports on visibility, share of voice, and the sources driving the narrative. Its straightforward interface and lower price point make it a strong choice for smaller businesses or marketing teams who need dedicated AI sentiment data.
6. Dialpad
Dialpad represents a different category of sentiment analysis, one focused entirely on the voice of the customer. The company's platform uses AI to transcribe and analyse sentiment from live customer support calls in real time. It can identify customer frustration and provide real-time coaching prompts to agents. While it does not track public or AI chatbot sentiment, it is an incredibly powerful tool for understanding and improving the sentiment of your existing customer base.
7. Brand24
Brand24 is a classic social listening and media monitoring tool. It excels at tracking brand mentions across social media, news sites, blogs, and forums at scale. It provides robust sentiment analysis for these public conversations, helping teams manage brand reputation, measure PR campaigns, and engage with customers. While it does not track sentiment within AI answers, it remains a best-in-class solution for the social media front of brand monitoring.
8. Chattermill
Chattermill is an AI-native platform for analysing customer feedback from a multitude of sources, including reviews, surveys, support tickets, and chat logs. It unifies this disparate feedback and uses its AI to identify sentiment and specific themes, such as problems with checkout or praise for a new feature. Like Dialpad, it focuses on the sentiment of known customers and users rather than the broader public or AI engines. It is a powerful choice for product and CX teams.
What a Modern AI Sentiment Report Must Contain
A modern AI sentiment report should provide far more than a simple pie chart of positive, negative, and neutral mentions. The data is more nuanced, and your reporting must reflect that to be actionable. As noted earlier, the vast majority of brand mentions in AI answers are neutral. This means simply tracking the existence of a mention is not enough; the context is everything.
A useful report for leadership should contain four key elements:
1. Sentiment by Share of Voice: How does your sentiment score compare to your top three competitors for the most important commercial queries? Are you mentioned positively while they are mentioned neutrally, or vice versa?
2. Source Attribution: What specific articles, Reddit threads, or review sites are the AI models citing when they form a positive or negative opinion? This is the most actionable part of the report, as it tells you where to focus your PR, content, and reputation management efforts.
3. Volatility Over Time: How stable is your sentiment? A chart showing sentiment fluctuations for a key prompt over 30 days can highlight instability and demonstrate why continuous monitoring is necessary, rather than one-off spot checks.
4. Verbatim Examples: The report must include direct, unedited quotes from the AI answers. A qualitative example of a poorly phrased recommendation is often more powerful for securing buy-in than a quantitative score alone. For guidance on structuring this data, see our AI visibility board report template.
The goal is to move from passive measurement to an active strategy. A good report does not just tell you the score; it gives you a roadmap for how to improve it. The ground has shifted beneath the feet of brand and marketing teams. The conversations that define your company's reputation are increasingly happening inside black-box AI models, and the old tools for listening in are no longer fit for purpose. Knowing where you stand in this new arena is the first step; influencing the outcome is the second.
Ready to see how your brand is portrayed by AI? Run a free AI visibility check today.
