Sentiment analysis tools read customer messages, calls or reviews and label the tone as positive, negative or neutral, often with a score. For a support team, the right tool depends on where your conversations live, which channels you need, and whether sentiment has to sit next to your QA scores or next to your survey and review data.
In short
- Start with what to look for: data source, channels, evidence, cause analysis and where results go.
- The shortlist below covers eight options: QA-built analytics, contact center conversation analytics, feedback analytics platforms and cloud APIs.
- Each tool gets the same three checks: what it does, strengths and limits, and its pricing model, all from the vendor’s own site.
- A scoring sheet and a worked accuracy check help you choose and then verify the tool once it is live.
- In the EU, tracking employees’ emotions from their voice is a prohibited practice; sentiment from written text is treated differently.
Disclosure: Kaizo is our product. We include it where it fits and say what the others do better. We read each vendor’s own website on November 4, 2026 for the facts below, and we did not use review-site ratings. Features change, so confirm anything critical with the vendor.
If you need the definitions first, our customer sentiment analysis guide explains how sentiment analysis works and where it fails. For how Kaizo scores sentiment analysis on every support message, see the product page.
What to look for in sentiment analysis tools
Look for five things: it reads your actual data source, covers every channel you run, shows the lines behind each label, separates causes from symptoms, and puts results where someone will act on them.
| What to check | Why it matters for support | Question for the vendor |
|---|---|---|
| Data source | Tools built for reviews and surveys see a different customer than tools built for tickets and calls | Which of our sources do you connect to natively? |
| Channels | A tool that is strong on voice can be thin on chat and email, and the reverse | Is sentiment on a chat as reliable as on a call? |
| Unit of analysis | A conversation-level label hides the moment the tone turned | Do you score per message, per sentence or per conversation? |
| Evidence | A negative label nobody can trace back to a line will not be trusted | Show me the lines behind this label |
| Cause and context | Sentiment tells you how it felt, not why | Can I see sentiment by contact reason, team or QA criterion? |
| Agent side | Customer sentiment and agent empathy are different measures | Do you measure the agent’s side, and how? |
| Languages | Accuracy often drops outside English | Which languages do you support for sentiment specifically? |
| Where results go | An alert nobody owns is noise | Can it filter, alert or export to the team that acts? |
One test cuts through most demos: ask the vendor to open a negative conversation and show you exactly which lines drove the label. Our guide to sentiment scoring in QA covers what a useful sentiment signal looks like inside a quality program.
The shortlist at a glance
This table gives each tool’s best fit and pricing model from its own site; none publishes a headline price for these products on the pages we read.
| Tool | Type | Best for | Pricing model |
|---|---|---|---|
| Kaizo | QA-built conversation analytics | Support teams that want sentiment next to QA scores on calls, chat and email | Per seat, quote on request |
| CallMiner | Contact center conversation intelligence | Voice-heavy contact centers that want acoustic signals and real-time guidance | On request |
| Qualtrics (contact center) | Experience management suite | Enterprises already running Qualtrics surveys | Plans, on request |
| Medallia | Experience management suite | Enterprises already on Medallia that want text analytics across channels | On request |
| SentiSum | Support ticket analytics | Teams on Zendesk, Freshdesk, Gorgias or Intercom that want tagging by cause | Scales with channels, users and ticket volume |
| Chattermill | Customer feedback analytics | CX teams combining tickets with surveys and reviews | Demo; no per-user fee |
| Thematic | Customer feedback analytics | Insight teams that want themes traceable to verbatims | Demo |
| Cloud NLP APIs | Developer building blocks | Teams with engineers building their own pipeline | Pay per use, public price lists |
1. Kaizo: best for sentiment next to QA scores
Kaizo is best for support teams that want sentiment, empathy and QA scores on the same conversations, because it scores every call, chat and email against your own criteria.
What it does
According to Kaizo’s sentiment page, customer sentiment and agent empathy are scored across 100% of messages, so frustration shows up as a trend.
You can filter by negative sentiment, high reopens or low CSAT to find conversations heading the wrong way, and root cause analysis shows whether a dip comes from a broken process, product friction or a skill gap. Kaizo has native Zendesk and Salesforce apps, and connects custom CRMs through a connector, its API or CSV import.
Strengths and limits
The strength is context: a negative conversation comes with its QA score and the criterion that failed, so you can tell a product problem from a coaching need.
The limit is timing and scope. Kaizo analyzes a conversation once it has ended, so it does not give agents live sentiment prompts during a call, and it works on helpdesk conversations rather than app reviews or social posts. If those are your main sources, a feedback analytics platform below fits better.
Pricing
The Kaizo pricing page says all plans are billed per seat, with three plans and an exact quote on request. There is no self-serve trial; you see the product in a live demo with sample data.
2. CallMiner: best for voice-heavy contact centers
CallMiner is best for large contact centers where most volume is voice, because it combines acoustic signals from the speaker’s voice with the words spoken, and offers real-time guidance for agents.
What it does
CallMiner’s sentiment analysis page says it scores sentiment by “combining the acoustic characteristics of a speaker’s voice with the context of the conversation”, using signals such as speaking pace and stress. Its homepage says the platform captures and analyzes all omnichannel interactions, including calls, emails, texts and surveys, and lists real-time guidance for agents.
Strengths and limits
Acoustic analysis catches tone that a transcript misses, a point we cover in what speech analytics is, and real-time prompts are a capability post-conversation tools do not offer.
The trade-off is that a voice-first platform is a larger deployment, and in the EU, voice-based emotion inference on your own agents falls under the AI Act ban covered below, so scope it to customers.
Pricing
CallMiner does not publish prices on its homepage and directs buyers to contact sales or request a demo.
3. Qualtrics: best for teams already on Qualtrics
Qualtrics is best for enterprises that already run their surveys in Qualtrics and want contact center conversations analyzed in the same experience management program.
What it does
Qualtrics’ contact center page says its natural language processing evaluates empathy, script adherence and sentiment, flags high-effort processes, and can monitor and score all interactions with AI, with more than 150 pre-built models. It works with on-premise, CCaaS and home-grown systems.
Strengths and limits
The strength is joining survey data and conversation data in one program, which suits organizations where the CX team already owns Qualtrics. The limit is weight: it is a large suite, and a support team that only needs sentiment on its helpdesk conversations may use a small part of it.
Pricing
The page links to flexible pricing plans but shows no figures.
4. Medallia: best for teams already on Medallia
Medallia is best for enterprises that already use Medallia for experience feedback and want text analytics, including sentiment and effort, across the same channels.
What it does
Medallia’s text analytics page says its natural language understanding analyzes “themes, sentiment, empathy, and nuanced emotions across dozens of languages and dialects”, with sentiment, empathy and effort as out-of-the-box measures you can customize. It mentions alerts on shifts in effort and risk, automated agent scoring and ticket prioritization.
Strengths and limits
Language coverage and prebuilt industry models are the strengths, plus low-code topic models for teams without data scientists. The limit is similar to Qualtrics: the value is highest when Medallia is already your experience platform.
Pricing
The page does not describe how text analytics is priced.
5. SentiSum: best for ticket tagging by cause
SentiSum is best for support teams on helpdesks such as Zendesk, Freshdesk, Gorgias or Intercom that want tickets tagged by topic, cause and sentiment without building a taxonomy by hand.
What it does
SentiSum’s sentiment analysis page describes automatic positive, negative and neutral tags on support tickets. Its homepage says it unifies tickets, chats, calls, reviews and surveys, classifies issues by cause, ranks them by impact and routes them to teams, and that a Quality Agent scores all conversations.
Strengths and limits
The breadth of helpdesk integrations is a strength for teams outside the Zendesk and Salesforce pair, and writing tags back to the helpdesk supports routing. Its QA scoring is newer territory for the company than its tagging, so check how far the scorecard can be shaped to your own criteria.
Pricing
The homepage says pricing “scales based on channels, users, and ticket volume”, with mid-market and enterprise plans.
6. Chattermill: best for tickets plus surveys and reviews
Chattermill is best for CX and voice-of-customer teams that need support tickets analyzed together with surveys, reviews and social feedback, ranked by impact on their metrics.
What it does
Chattermill’s homepage says it “analyzes all your surveys, reviews, tickets and calls, ranks issues by impact on your metrics”, tags feedback into themes with sentiment, and claims more than 65 native integrations, including Zendesk, Intercom, Salesforce, Qualtrics and Trustpilot.
Strengths and limits
It sees the customers who never contact support, which a helpdesk tool cannot. Chattermill itself says it is a poor fit for teams not consistently collecting more than 5,000 pieces of feedback a month, and it is not a QA tool, so agent-level scoring sits elsewhere.
Pricing
Demo-led, with no per-user fee according to the homepage.
7. Thematic: best for themes you can trace to verbatims
Thematic is best for research and insight teams that want themes built from customers’ own words rather than a fixed list, with every theme traceable to the comments behind it.
What it does
Thematic’s homepage says it builds themes from customer language, “not a predefined taxonomy”, shows sentiment per theme, and takes surveys, support tickets, call center transcripts, app reviews and CRM notes, with PII redaction and translation built in.
Strengths and limits
Traceability from theme to verbatim is the strength, which is what makes analysis credible to product teams. It is an analysis layer for insight teams rather than a tool support team leads use daily on individual conversations. Our explainer on text analytics for support covers the method behind theme detection.
Pricing
Demo-led; the homepage shows no prices.
8. Cloud NLP APIs: best for building your own
Cloud APIs from Google, Amazon and Microsoft are best for teams with engineers who want sentiment as a building block inside their own data pipeline, and who will build the dashboards, joins and alerts themselves.
What it does
Google Cloud Natural Language returns a score from -1.0 to 1.0 plus a magnitude for the strength of emotion, and can score sentiment per entity. Amazon Comprehend returns positive, negative, mixed or neutral with a confidence score for each. Azure Language sentiment analysis returns labels with confidence scores at sentence and document level, plus opinion mining by aspect.
Strengths and limits
You control the data and the cost per message, and the output feeds anything you build. You also build everything else: connectors, the link to contact reasons and QA, the interface and the follow-up. Plan for change too: Microsoft says sentiment analysis and opinion mining retire from Azure Language on March 31, 2029, with new projects directed to Microsoft Foundry.
Pricing
Pay per use, with public price lists on each provider’s site.
How to choose: a scoring sheet
Score each shortlisted tool from 1 to 5 on the criteria below, multiply by the weight, and add up; adjust the weights to your situation before you start, not after the demos.
| Criterion | Weight (support team example) | Tool A | Tool B | Tool C |
|---|---|---|---|---|
| Connects to our main data source natively | 25% | |||
| Covers all our channels at the same depth | 20% | |||
| Shows the lines behind each label | 15% | |||
| Links sentiment to contact reason, team and QA | 15% | |||
| Agent-side measure (empathy) | 10% | |||
| Languages we support | 10% | |||
| Results reach the people who act | 5% |
A simple decision rule sits behind the weights.
If your sentiment questions are about support conversations and coaching, weight data source and QA linkage highest.
If they are about the whole customer base, including people who never contact you, weight surveys and reviews highest and expect a feedback analytics platform to win. For a fuller evaluation plan, our conversation intelligence buyer’s guide adds the questions on security, setup and cost.
How to check sentiment accuracy once a tool is live
Check accuracy by having two people label a random set of conversations, then comparing the tool’s labels with theirs, with most attention on how many genuinely negative conversations the tool caught.
The numbers below are an illustrative example we calculated, not a vendor’s results.
The sample. Two reviewers label 200 recent conversations as positive, neutral or negative, and settle disagreements together. They agree that 40 are negative.
The comparison. The tool matches the reviewers on 158 of 200 conversations: 79% overall agreement. It flags 45 conversations as negative, and 30 of those are among the reviewers’ 40.
What it means. The tool catches 30 of 40 negative conversations (75%), and 30 of its 45 negative flags are right (67%). The overall 79% looks fine, but one in four unhappy customers is missed, and one in three alerts is a false alarm.
Read the misses before you change anything. Sarcasm, mixed messages (“thanks, but this is the third time”) and non-English conversations are the usual causes. Repeat the check every quarter, and after any change to the model or your channels. Our explainer on how accurate AI QA is uses the same agreement logic for QA scores.
Sentiment, emotion and the EU AI Act
Under the EU AI Act, inferring employees’ emotions from biometric data such as their voice has been prohibited since February 2, 2025; sentiment from written text is outside that ban.
The ban is in Article 5(1)(f) of the AI Act, and the European Commission’s AI Act overview lists “emotion recognition in workplaces and education institutions” among the practices prohibited from February 2025.
The Commission’s February 2025 guidelines on prohibited practices, which are guidance rather than law, give a call center using voice recognition to track its employees’ emotions as a prohibited example. According to CMS’s summary of those guidelines, tracking customers’ emotions by voice and inferring emotions from written text are outside the prohibition.
Two practical consequences for a support team in the EU. Do not switch on voice-based emotion detection for your agents; scope acoustic features to the customer side. And from August 2, 2026, deployers of emotion recognition systems must inform the people exposed to them under Article 50(3). This is a summary, not legal advice, so check your setup with counsel.
How to start choosing a sentiment analysis tool
Start by writing down the one decision sentiment should improve, such as which conversations a team lead reviews first, then shortlist only the tools that connect to the data behind that decision.
If the decision is about support conversations, coaching and quality, start with tools that put sentiment next to your QA scores. See how Kaizo scores sentiment and empathy on every message in a live demo with sample data. Let’s talk.
Frequently asked questions
Can ChatGPT do sentiment analysis?
Yes. A large language model can label the sentiment of a message if you prompt it, and often handles sarcasm and mixed messages better than older models. On its own, it is not a tool: you still need to connect your conversations, keep the labels consistent over time, store the results and check accuracy against human labels.
What are the three main types of sentiment analysis?
The three most common types are polarity (positive, negative or neutral), fine-grained (a scale such as very negative to very positive) and aspect-based (sentiment about a specific topic, such as delivery or pricing). Emotion detection, which names feelings such as frustration, is often listed as a fourth.
Is sentiment analysis the same as CSAT?
No. CSAT is what customers tell you when they answer a survey; sentiment analysis reads the tone of the conversation itself, including customers who never answer. The two disagree often enough to be useful together, as our guide to measuring satisfaction without asking explains.
Do sentiment analysis tools work in languages other than English?
Most do, but coverage and accuracy vary by language and by feature, so check the vendor’s language list for sentiment specifically rather than for the product overall. Then run the accuracy check above on a sample in each of your main languages.
Should you measure agent sentiment as well as customer sentiment?
Measure agent behavior, such as empathy and tone, as a QA criterion from the transcript rather than inferring the agent’s emotions. In the EU, inferring employees’ emotions from biometric data such as voice is prohibited, while scoring what the agent wrote or said against a rubric is a quality review.
Keep reading
- What is sentiment analysis in customer service?, the short definition
- Empathy in customer service, with statements agents can use
- What is voice of the customer?, on combining feedback sources
- Conversation analytics for support, sentiment, QA and root cause in one view
