5 best text analytics tools for analysts and data engineers

Support tickets, Slack threads, and product reviews capture what customers think, but unstructured text doesn’t fit neatly into rows and columns. Once engineers transform the text into usable formats, business analysts can use text analytics tools to pull sentiment and themes from the data. 

The right text analytics tool has analysis methods and data connections that match how you collect feedback. Here’s a breakdown of five options, including the pros and cons of each, so that you can find the best route for your needs.

What is text analytics software?

Text analytics software is a category of tools that converts written text into structured fields like sentiment labels and topic tags, typically through natural language processing (NLP) and machine learning. It’s useful for aims like sentiment analysis and trend detection across sources like reviews and support tickets.

Text analytics tools read customer comments in bulk to help organizations pinpoint the following:

• Root causes: Text analytics links specific complaints, like checkout errors, to drops in satisfaction scores, so product managers know what to fix first.
• Sentiment trends: Sentiment scores tracked before and after each product update show customer experience (CX) teams whether a change improved the experience or the feature needs fixing.
• Early risk signals: Alerts on rising mentions of outages or refund demands give support teams time to act before those issues escalate.

Key features to look for when choosing a text analytics tool

Tools have different processing speeds, pricing, and scoring. When comparing tools for text analytics, consider the following specifications to choose the right option for your team:

• Sentiment granularity: Aspect-based scoring rates each topic in a comment. That way a tool can recognize if a reviewer praised delivery while criticizing packaging, for example.
• Theme discovery: Platforms either build themes from the words customers use or sort comments into a category list your team defines and updates.
• Processing speed: Teams evaluating scalable AI tools for real-time text analytics need API-based scoring for alerts, while batch jobs suit monthly reports.
• Pricing model: Vendors charge by comment volume or API requests, so costs increase as text data volume grows.

5 best text analytics tools

Text analytics tools range from feedback analytics platforms for CX and insights teams to NLP APIs for developers. Here are five text analytics software tools worth considering.

1. Chattermill

Chattermill is a feedback analytics platform that unifies surveys, reviews, social posts, and customer service platforms. Its Lyra AI model tags themes and scores sentiment separately for each aspect a customer mentions.

Chattermill analyzes feedback in more than 100 languages, so global brands can track one theme set across markets. Impact analysis links themes to Net Promoter Score (NPS) and customer satisfaction score changes to show which complaints affect scores most.

Pros:

• Anomaly alerts: Chattermill flags spikes in negative themes, so support teams respond before an issue spreads.

• Quick ramp-up: Reviewers note that new users have an easy initial setup process.
• Automatic topic discovery: Chattermill’s AI can flag new topics in feedback without anyone setting up a tag first, so insights teams spot emerging issues early.

Cons:

• Sarcasm and mixed tone: Reviewers report that sentiment scoring sometimes misreads sarcastic or mixed reviews, so analysts have to correct some tags manually.

Best for: Chattermill works well for enterprise CX and product teams at consumer brands with high feedback volume across markets.

2. Thematic

Thematic is a feedback analytics platform that discovers themes from the words customers use, so insights teams don’t have to set up categories in advance. Analysts can view the raw comments linked to each theme to verify findings before presenting them. Staff across the company can ask plain-language questions and get answers traced to source comments.

Score change analysis estimates each theme’s contribution to a change in NPS or another tracked score.

Pros:

• Theme tracking: Reviewers find that Thematic makes it easy to compare themes from one survey round to the next to confirm whether a product fix changed perception.
• Shareable summaries: Thematic writes short AI summaries for each theme and dashboard view, so analysts can send findings to stakeholders without drafting a report from scratch. 
• Flexible data import: Thematic accepts data through one-click integrations, file uploads, an API, or custom integrations.

Cons:

• Setup effort: Reviewers find that multi-source setups and initial theme tuning take longer than expected.

Best for: Insights teams that analyze thousands of customer comments per data set and report findings to leadership may find Thematic to be suitable.

3. Qualtrics XM (Text iQ)

Qualtrics XM is an experience management platform, and Text iQ is its built-in text analysis tool. Text iQ suggests topics from response patterns, and analysts can edit them or start from industry libraries. The tool then tags open-ended responses with topics and, on advanced licenses, scores sentiment per response and per topic.

Pros:

• Ratings and comments together: Analysts see comments beside rating scores from the same survey.
• Survey flows: Text iQ-powered survey flows score sentiment while respondents are still answering, so follow-up questions adapt immediately.

Cons:

• License-gated sentiment: Sentiment analysis and survey flows require an Advanced Text iQ license, so teams on a basic license have to label sentiment manually or upgrade.

Best for: It makes sense for CX teams that already run programs in Qualtrics and want text analysis without exporting responses.

4. Lexalytics (InMoment)

Lexalytics (part of InMoment since 2021) helps data science teams train categorization and entity extraction on industry vocabulary and adjust sentiment scoring. 

Its Salience engine runs as software libraries on your own servers. Its Semantria API delivers Salience’s sentiment analysis and categorization as a cloud service.

Pros:

• Data residency control: On-premises Salience keeps sensitive text, like patient notes, inside your own infrastructure.
• Embeddable API: Software vendors can build Semantria into their products and offer text analytics to customers.
• Spotlight dashboards: Lexalytics’ Spotlight application stores analyzed documents and turns results into shareable visualizations.

Cons:

• Developer effort upfront: Salience ships as a software library, so engineers must integrate it before analysts see any output.

Best for: Regulated enterprises and software vendors that must run text analytics on their own infrastructure can appreciate Lexalytics.

5. IBM Watson Natural Language Understanding

IBM Watson Natural Language Understanding is an NLP service on IBM Cloud that pulls entities, keywords, categories, and relations out of unstructured data. Emotion analysis labels text with feelings like joy, anger, fear, and sadness, which adds detail that positive and negative scores leave out.

Pros:

• Less model maintenance: Prebuilt extraction for entities and sentiment is ready to use, so developers don’t have to build and maintain their own NLP models.
• Unified workspace: IBM Watson Studio covers data preparation, visualization, model runs, and deployment in one place, which removes the need to switch between separate tools.

Cons:

• Steep learning curve: Reviewers note that building and training custom models is a complex process that requires specialized knowledge.

Best for: Enterprise developers and data science teams that need entity and emotion extraction within their applications may prefer it.

How Fivetran centralizes and cleans text data before analysis

Text analytics platforms produce weaker results when customer comments arrive late from scattered systems. Data engineers end up writing a custom extraction script for each channel, adding to the team’s ongoing maintenance work.

Fivetran replaces custom extraction scripts for each feedback channel with prebuilt connectors for sources like Zendesk, Slack, Gmail, and Salesforce, plus unstructured file replication for contracts and similar documents.

Data engineering teams can use integrated scheduling in Fivetran Transformations, so dbt models can run after each sync. The prebuilt Zendesk data model, for example, splits ticket text into chunks ready for sentiment analysis in Snowflake Cortex or OpenAI models.

Fivetran’s own product team syncs feature requests from Salesforce and Jira into BigQuery, where AI agents sort the requests to manage customer feedback at scale.

Start a free trial with Fivetran to sync support tickets and chat messages to your warehouse, so analysts can study customer feedback without manual data preparation.

FAQ

What is the difference between text analytics and text mining?

Text mining is the process of finding patterns in large text collections, like word associations and document clusters, usually during exploratory research. Text analytics refers to identifying patterns in text to answer a business question or inform decision-making.

What are scalable AI tools for real-time text analytics?

Cloud NLP services like Amazon Comprehend score comments through synchronous API calls as they arrive and run asynchronous jobs for large backlogs. Warehouse-native AI functions score new rows right after a pipeline tool like Fivetran loads them, which keeps text inside your existing data platform.

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