9 Best Sentiment Analysis Tools to Better Understand Your Customers in 2026

As AI solutions are on the road to full adoption in the business world, sentiment analysis and sentiment scoring are both on the rise.

The model is simple: AI tools, at least in their current form, are great at looking over large datasets and coming up with conclusions. Therefore, analyzing customer behavior, identifying patterns, and coming up with predictive models are logical next steps.

Sentiment analysis tools turn customer text, conversations, and product signals into one or more sentiment scores or risk labels you can use in support, CX, and customer success workflows. This list focuses on tools that work with SaaS customer data, event streams, and feedback, not social listening or brand monitoring, and it compares them by data sources, scoring output, integrations, and setup effort. Custify is included because it ties sentiment and churn risk to account workflows and automation inside a customer success platform.

In this article, we used “best” to mean: insightful, usable outputs (sentiment score backed by evidence and suggestions), strong integrations, and workflows that trigger actions, not dashboards that look nice.

What Are Sentiment Analysis Tools?

Sentiment analysis tools are software solutions able to take on large datasets and determine how customers feel about a product, service, or brand. Most sentiment analysis tools generate their insights by looking at product data, while some do it by looking at social media presence, reviews, and other public testimonials from a brand’s customers and general audience. In this list, we will be focusing specifically on customer sentiment scoring tools that analyze product data.

Best Sentiment Analysis Tools

1. Fullstory

Fullstory is a sentiment analysis tool that seamlessly integrates sentiment scoring with product analytics tools to offer a full view of customer journeys. Fullstory has advanced sentiment scoring capabilities, being able to track frustration signals, identify user experience issues, and help companies unify user journeys and remove friction points.

Best for: SaaS, mobile apps, any software products, employee experience analytics, data ecosystem tools

How Sentiment Analysis Works in Fullstory

Fullstory tracks frustration signals and sends automatic alerts to account owners or CSMs. With Fullstory, you are able to track:

• Dead clicks
• Error clicks
• Rage clicks
• Scroll depth
• Product issues and fixes
• Abandoned forms
• Network errors
• Crashes

Fullstory does not provide a sentiment score, though you can set up a tracked metric that indicates the overall customer sentiment. Instead, it focuses on how your tracked metrics have evolved over time. Fullstory captures data, user experiences, app behavior, and product analytics directly from your website or software. Fullstory also allows server-side integrations for additional datapoints.

Fullstory Pricing

Fullstory offers three separate plans for its Analytics, Workforce, and Anywhere products, all quote-based. The Analytics product, which includes the Sentiment Analysis model, is further divided into three additional plans: Business, Advanced, and Enterprise.

Fullstory Integrations

Key Fullstory integrations for sentiment analysis: Salesforce, Qualtrics XM, Adobe Experience Cloud, Gainsight, Google Analytics, Intercom, Google Tag Manager, Jira, Help Scout, Shopify, Slack, Segment, Squarespace, Trello, Zendesk

2. Custify

Custify is a customer success software platform that helps software companies monitor customer journeys, reduce churn, improve onboarding, and grow revenue. Through CustifyAI, customer representatives can get sentiment scores based on varied customer and product datapoints. These insights allow you to create automation flows triggered by sentiment analytics, customer health scores, and other relevant KPIs.

Best for: customer success teams, SaaS apps, B2B SaaS, software startups, companies looking to reduce churn

How Sentiment Analysis Works in Custify

CustifyAI can analyze customer interactions, product engagement metrics, lifecycles, customer health scores, and other tracked signals to determine how a customer is doing at any specific moment. It generates a numerical Risk Score, as well as an Engagement Score. Furthermore, CustifyAI can tell you a customer’s Churn Risk (low/mid/high) and conduct a Churn Analysis directly in the account dashboard. Lastly. CustifyAI has an early warning system for both negative and positive signals, like concerning sentiment score drops, low engagement rates, or encouraging sentiment score growth.

Custify Pricing

Quote-based, with plans tailored to fit any organization size – from small startups to large-scale enterprises. Contact sales for more details.

Custify Integrations

Key Custify integrations for sentiment analysis: AWS Athena, Aircall, Mixpanel, Snowflake, AWS Redshift, Azure Synapse, Jira, Salesforce, Hubspot, Zapier, Teams, Outlook, Gmail, Zoho, Slack, Zendesk, Segment, Intercom, Freshdesk, Help Scout, Stripe. More information about integrations here.

3. Unwrap AI

Unwrap AI is a customer intelligence platform that helps businesses uncover AI-powered insights from democratized data sources. It centralizes customer feedback, surveys, communication, and other data sources and allows users to build, customize, and share reports and other data points with the entire team.

Best for: software products, apps, SaaS products, B2B apps, product management teams, customer success, or CX teams

How Sentiment Analysis Works in Unwrap AI

Unwrap AI gathers all relevant data in one place and uses machine learning (ML) as well as natural language processing (NLP) algorithms to turn that data into action items. Regarding sentiment scoring, the tool categorizes all feedback into three categories: positive, negative, and neutral.

Unwrap AI Pricing

Unwrap AI offers flexible plans starting at $24,000 per year with a 30-day trial (pricing last checked mid-January 2026).

Unwrap AI Integrations

Key Unwrap AI integrations for sentiment analysis: Discord, FreshDesk, Google Maps, Help Scout, Hubspot, Instagram, Jira, Pendo, Qualtrics, Reddit, Salesforce, Zapier, X, Zendesk, Shopify, Talkdesk, Facebook Groups

4. Microsoft Azure Language

Unlike the previous entries on the list, Microsoft Azure’s Language tool (formerly Azure Text Analytics) is part of the Microsoft Foundry Tools. Azure Language is a cloud-based service that helps integrate AI into your apps to extract information for classification, sentiment scoring, conversational analytics, and natural language understanding (NLU) and processing (NLP).

Best for: data scientists and data governance, legal teams, SaaS companies, healthcare companies, business intelligence, customer success and support teams, marketing

How Sentiment Analysis Works in Azure Language

Azure Language works by integrating with your app through the web-based Microsoft Foundry, REST APIs, and client libraries. It uses Microsoft’s own Natural Language Processing algorithm and can be used to create agents via the Azure Language MCP server.

Microsoft Azure Language Pricing

Quote-based, with an extensive breakdown of price point per 1,000 text records. Those interested are able to request a pricing quote or estimate their cost using the table available at the link above.

Microsoft Azure Language Integrations

Integrates with applications through SDK and REST API.

5. IBM Watson Natural Language Understanding

IBM’s Watson Natural Language Understanding is similar to our previous entry in terms of using NLU and NLP to condense the meaning out of large sets of unstructured text. Where it differs is in adoption, features, and possibilities. IBM Watson includes text analytics, entity recognition, categories and classifications, understands high-level concepts, emotions, and sentiments, and has slightly more advanced semantic understanding.

Best for: data scientists and data governance, SaaS companies and developers, business intelligence, customer success and support teams

How Sentiment Analysis Works in IBM Watson

IBM Watson uses natural language processing to understand text and return specific information and insights about it. It works through specific code requests depending on what you want to obtain. For example, you can analyze the sentiment of a document through the sentiment.document command, which returns a simple score between 1 and 100.

IBM Watson Pricing

IBM Watson Natural Language Understanding is part of the IBM Cloud package. It offers two plans: Lite and Standard. The Lite plan is limited to 30,000 NLU requests and one custom model per month. The Standard Plan can handle over 5 million requests per month at a price point of $0.003 per request.

IBM Watson Integrations

Integrates with applications through SDKs and the Watson REST API.

6. Sprig

Sprig is a powerful AI-native survey solution with advanced sentiment analytics and monitoring capabilities. While primarily a product designed to capture customer feedback, one of its key use cases is measuring customer experiences over time by targeting users with custom in-product surveys designed for key moments in their customer journey. Then, the results can be interpreted over time with the use of AI survey summaries.

Best for: customer support and success teams, SaaS apps, B2B, and customer support companies

How Sentiment Analysis Works in Sprig

Sprig allows you to identify critical user journeys in your product, website, or app, along with the users you’re most keen to hear from. Next, it lets you send in-product surveys. Survey results are then centralized and summarized through Sprig AI, allowing you to see how user engagement and customer sentiment evolve over time, along with specific survey replays.

Sprig Pricing

Sprig offers three plans, Research Core, Digital Experience, and Digital Behavior, all quote-based.

Sprig Integrations

Key Sprig integrations for sentiment analysis: Notion, Slack, Zapier, Figma, Adobe XD, Miro, iOS, Android, Segment, Google Tag Manager, Mixpanel, Amplitude

7. Enterpret

Enterpret is a customer intelligence platform designed with the express purpose of unifying customer feedback and analyzing it with AI to help businesses better understand their customers. Although Enterpret is a complex solution offering features such as adaptive taxonomy and data enrichment, one of its core uses is as a Voice of the Customer software that gathers all customer feedback in one place, generating a sentiment score along with deep, accurate insights.

Best for: running voice of the customer programs, customer success teams, SaaS apps, medium-to-large B2B companies, product teams, engineering teams, customer experience teams

How Sentiment Analysis Works in Enterpret

Enterpret gathers all customer feedback, tickets, surveys, and reviews into its customer intelligence platform. It then tags feedback through AI / manual tagging and organizes it by theme, features mentioned, and potential impact. Lastly, it generates deep insights based on customer sentiment. It also offers an AI insights agent, which can then be queried about any customer data or issues.

Enterpret Pricing

Quote-based.

Enterpret Integrations

Key Enterpret integrations for sentiment analysis: Amplitude, Jira, Slack, Mixpanel, Salesforce, Snowflake, Zendesk, AWS Connect, iOS App Store, Discord, Facebook, G2, Google My Business, Intercom, Hubspot, Instagram, LiveChat, Qualtrics, Reddit, Segment, SurveyMonkey, Sprig, TikTok, TrustPilot, X, Zapier, Zendesk

8. Lexalytics

Lexalytics is a Natural Language Processing tool designed to be integrated into applications directly through its API. It provides sentiment analysis, text and document categorisation, entity extraction, and intention detection.

Best for: SaaS apps, hotel apps, restaurant apps, retail apps, pharma apps, voice of the customer/employee programs

How Sentiment Analysis Works in Lexalytics

Lexalytics’s Semantria wraps the Salience engine (offering text analytics and NLP) into a RESTful API. This allows Lexalytics to integrate directly into your product, app, or platform.

Lexalytics Pricing

Quote-based.

Lexalytics Integrations

Lexalytics’s Salience engine integrates into any app through its Semantria REST API.

9. XM Discover (Formerly Clarabridge)

XM Discover (formerly known as Clarabridge) is a platform that allows you to centralize customer feedback across different channels (such as chat, voice calls, emails, customer reviews, and more) and understand it using machine learning algorithms.

Best for: analyzing vast sets of customer data, data scientists and data governance, SaaS apps, medium-to-large B2B companies, product teams, customer experience teams

How Sentiment Analysis Works in XM Discover

XM Discover features 3 built-in applications: Connectors, Designer, and Studio. Through connectors, you are able to integrate all the tools from which you want to gather customer data. In Designer, you are able to determine specifically how your customer feedback is categorized as well as the rules for capturing data, feedback topics, and feedback channels. Designer is also where you are able to set your rules for customer sentiment analysis. Finally, Studio is the app where you will visualize all the data you’ve previously collected. It allows you to create custom, shareable dashboards featuring data based on previously set rules.

XM Discover Pricing

Not publicly available. Quote-based.

XM Discover Integrations

Key XM Discover integrations (connections) for sentiment analysis: Facebook, Genesys Cloud, Qualtrics, Salesforce, TripAdvisor, Trustpilot, Zendesk, Brandwatch

What Is Sentiment Analysis?

Sentiment analysis means analyzing large quantities of data and text to understand how it comes across and what sentiment it indicates – positive, negative, or neutral. Companies often employ sentiment analysis through various algorithms and solutions with the scope of determining their customers’ or employees’ sentiment regarding their organization, product(s), and overall brand.

Why Is Sentiment Analysis Important?

Customer sentiment analysis is vital as it indicates how clients and users feel about a brand down to the most minute details. The analysis model can uncover hidden truths, showcase the overall sentiment regarding the brand, and help leaders go beneath the surface-level niceties to find out how people really feel about their company, brand, or product(s).

What Is Sentiment Analysis in AI?

With the recent advancements in AI and large language processing models, sentiment analysis has become significantly easier while allowing companies and customer representatives (account managers, CSMs, etc.) to go more in-depth and analyze far larger sets of data than previously available through simpler machine learning algorithms.

How to Choose the Best Sentiment Analysis Tool

To make sure you get the right sentiment analysis tool for your use case, choose one based on:

• Data sources you already have (tickets, calls, product events, surveys, reviews)
• Output you need (sentiment score, emotion detection, label, explanation, alerting)
• Where actions happen (CS platform, helpdesk, data warehouse, customer feedback too, customer intelligence platform, customer survey tool)
• Data governance (PII handling, retention, audit trail)
• Calibration, or how you review false positives and sentiment analysis drift over time

If you need sentiment scoring inside CS workflows

With the right sentiment scoring tool inside your CSP, it’s a quick road to reducing churn, showcasing interesting usage patterns, and making better overall business decisions.

If you’d like to learn more about CustifyAI and its sentiment analysis feature, set up a quick 15-minute call, and our team will be happy to walk you through it.