Text analytics is the process of deriving meaningful insights from text data using various computational techniques.
Text analytics tools help businesses analyze large amounts of text data to uncover insights and trends.
These natural language processing tools can process customer reviews, social media comments, and emails to understand sentiment and key topics.
Many data analytics platforms offer features like keyword extraction, sentiment analysis, and topic modeling for better content strategy.
Using text mining solutions helps businesses gain a competitive edge by turning raw text into valuable insights.
Here we have listed the top 10 up-to-date text analysis tools for 2025.
List of Best Text Analytics Tools
MonkeyLearn
MonkeyLearn is a no-code AI-driven text analysis platform designed to extract valuable insights from unstructured data.
It provides tools for sentiment analysis, keyword extraction, and topic classification, making it ideal for businesses analyzing customer feedback, reviews, and social media content.
With an intuitive drag-and-drop interface, even non-technical users can build and train models to process text data.
Key Features:
- Pre-trained and customizable NLP models
- Easy integration with tools like Google Sheets and Zendesk
- Drag-and-drop interface for model training
- Sentiment and intent analysis
- Data visualization dashboard
Pros:
✔ No coding required
✔ Highly customizable models
✔ Seamless integration with business apps
Cons:
✖ Limited free plan
✖ May require training for more complex models
| Feature | Details |
| Launch Date | 2015 |
| Starting Price | $299/month |
| Capabilities | Sentiment analysis, keyword extraction, topic modeling |
| Public Reception | Positive for ease of use, slight learning curve for advanced features |
| Ideal Users | Businesses analyzing customer feedback |
Lexalytics
Lexalytics provides AI-driven text analysis solutions, helping businesses turn complex text into useful insights.
Their platform includes sentiment tracking, entity recognition, and intent detection, supporting 29 languages.
Their industry-specific models improve sentiment accuracy for sectors like hotels, retail, and pharma.
Key Features:
- Advanced NLP capabilities with entity recognition
- Multi-language support
- Customizable text analysis models
- On-premise and cloud deployment options
- Industry-specific sentiment models
Pros:
✔ High scalability for enterprise use
✔ Strong linguistic processing power
✔ Supports multiple languages
Cons:
✖ Requires technical expertise for customization
✖ Expensive for small businesses
| Feature | Details |
| Launch Date | 2003 |
| Starting Price | Custom pricing |
| Capabilities | Deep linguistic analysis, entity recognition |
| Public Reception | Positive among enterprises, considered expensive for small businesses |
| Ideal Users | Large-scale data-driven organizations |
IBM Watson NLP
IBM Watson Natural Language Understanding helps analyze text data using AI. It extracts key details like sentiment, emotions, categories, and relationships.
The tool improves workflow by reducing time spent on data analysis by 50% and increasing revenue by 5% yearly.
Businesses see a 383% ROI over three years, with $6.13 million in cost savings. It supports 13 languages and integrates with cloud or private systems.
Key Features:
- AI-powered text analysis
- Pre-trained models for industries like finance and healthcare
- Supports multiple languages
- Cloud and on-premise options
- Integrations with IBM Cloud services
Pros:
✔ High accuracy with AI-powered models
✔ Reliable for large enterprises
✔ Industry-specific solutions
Cons:
✖ Expensive for small businesses
✖ Requires expertise to set up and customize
| Feature | Details |
| Launch Date | 2011 |
| Starting Price | Custom pricing |
| Capabilities | AI-driven sentiment analysis, entity recognition |
| Public Reception | High accuracy, but complex setup |
| Ideal Users | Large enterprises in regulated industries |
MeaningCloud
MeaningCloud Text Analytics for Excel helps you analyze text easily in spreadsheets. It extracts key topics, identifies sentiment, and classifies text by themes.
The tool supports over 160 languages and groups similar texts using clustering. It also detects emotions, customer intent, and industry-specific terms.
Users can access Vertical Packs for targeted analysis, such as customer feedback or financial language.
Key Features:
- Sentiment and intent analysis
- Entity and concept recognition
- Industry-specific text analysis models
- API for integration with CRM and BI tools
Pros:
✔ Affordable pricing
✔ Strong multi-language support
✔ API for developers
Cons:
✖ Interface could be more intuitive
✖ Limited free-tier processing
| Feature | Details |
| Launch Date | 2015 |
| Starting Price | $99/month |
| Capabilities | Sentiment analysis, entity recognition |
| Public Reception | Good for developers, UI could improve |
| Ideal Users | Researchers, marketing professionals |
RapidMiner
RapidMiner is a robust data science platform that includes text analytics as part of its machine learning and AI toolkit.
It allows businesses to extract insights from unstructured text data, making it valuable for sentiment analysis, document classification, and predictive analytics.
The platform supports extensions like Python Scripting and Deep Learning. High DPI screen support enhances usability.
Key Features:
- Drag-and-drop workflow builder for easy model creation
- Pre-built machine learning models for text analytics
- Seamless integration with databases and third-party applications
- Scalable architecture for handling large datasets
- Advanced predictive modeling capabilities
Pros:
✔ No coding required for basic models
✔ Comprehensive machine learning and text analytics features
✔ Supports big data processing
Cons:
✖ High learning curve for beginners
✖ Expensive for small businesses
| Feature | Details |
| Launch Date | 2006 |
| Starting Price | $2,500/year |
| Capabilities | Text analytics, predictive modeling, big data support |
| Public Reception | Positive among data scientists, complex for beginners |
| Ideal Users | Businesses needing advanced text analytics and AI |
Google Cloud Natural Language API
Google Cloud Natural Language API provides powerful machine learning-based text analysis services.
It enables businesses to extract meaning from text, identify entities, and analyze sentiment efficiently.
AutoML lets users build custom models without coding, while the Natural Language API applies pre-trained analysis to apps.
Key Features:
- Sentiment analysis and entity recognition
- Syntax analysis and content classification
- Multi-language support
- Deep integration with Google Cloud services
- Real-time processing via API
Pros:
✔ Scalable and efficient for enterprise use
✔ High accuracy due to Google’s AI models
✔ Easy integration with other Google tools
Cons:
✖ Requires API knowledge for full utilization
✖ Pricing can be high for large-scale processing
| Feature | Details |
| Launch Date | 2016 |
| Starting Price | $0.50 per 1,000 records |
| Capabilities | Sentiment analysis, entity recognition, content classification |
| Public Reception | Highly accurate, but requires API integration |
| Ideal Users | Developers, large-scale businesses |
Amazon Comprehend
Amazon Comprehend is a fully managed NLP service that uses machine learning to analyze text data.
It is widely used for automated document processing, customer feedback analysis, and legal text review.
Brands like LexisNexis and Chisel AI rely on it for automation and security. AWS offers flexible pricing, free training, and expert support to help users get started.
Key Features:
- Machine learning-powered entity recognition and topic modeling
- Sentiment and key phrase detection
- Auto-classification of documents
- Multi-language support
- Seamless integration with AWS services
Pros:
✔ Fully managed and scalable
✔ Secure and compliant with industry standards
✔ AI-driven accuracy
Cons:
✖ Limited offline functionality
✖ Requires AWS knowledge for seamless use
| Feature | Details |
| Launch Date | 2017 |
| Starting Price | $0.0001 per character |
| Capabilities | NLP-based text analytics, document classification |
| Public Reception | Reliable for AWS users, API complexity noted |
| Ideal Users | Enterprises, developers using AWS |
SAS Text Miner
SAS Text Miner is an enterprise-grade analytics tool designed for deep text mining and data visualization.
It is particularly valuable for healthcare, finance, and legal industries that need to process large amounts of text.
Users can access technical support, hot fixes, and a community for help. The latest version is SAS Text Miner 15.3, which includes updates for better performance.
Key Features:
- Machine learning and linguistic rules for text analysis
- Customizable classification models
- Advanced data visualization tools
- Multi-language processing
- Scalable architecture for big data
Pros:
✔ Highly powerful for enterprise applications
✔ Customizable for industry-specific needs
✔ Advanced text mining features
Cons:
✖ Expensive compared to competitors
✖ Requires training for new users
| Feature | Details |
| Launch Date | 2004 |
| Starting Price | Custom pricing |
| Capabilities | Deep text mining, predictive analytics |
| Public Reception | Excellent for enterprises, high cost a concern |
| Ideal Users | Large-scale businesses, finance and healthcare industries |
TextRazor
TextRazor processes text using advanced language analysis to extract key details quickly. It identifies entities, links them to real-world knowledge, and classifies topics in multiple languages.
The system analyzes sentence structure, relationships, and word dependencies to provide deep insights.
The bank later admitted this was a reporting error. Experts stress that such discrepancies affect investor trust, emphasizing the need for precise financial disclosures.
Key Features:
- Named entity recognition and entity linking
- Sentiment analysis and topic classification
- Multi-language support
- API-based integration with other platforms
- AI-powered keyword extraction
Pros:
✔ High-speed processing
✔ Detailed and customizable NLP models
✔ Developer-friendly API
Cons:
✖ Requires coding knowledge
✖ Limited graphical interface
| Feature | Details |
| Launch Date | 2012 |
| Starting Price | $49.99/month |
| Capabilities | Entity linking, text classification, keyword extraction |
| Public Reception | Popular with developers, lacks UI for non-technical users |
| Ideal Users | AI researchers, software engineers |
AYLIEN
AYLIEN is a powerful text analysis API that provides tools for sentiment analysis, entity recognition, and news intelligence. It is widely used for market research and media monitoring.
With 4,500+ category tags and 5.6+ million entity tags, it improves search efficiency by 95%, using sentiment analysis, event clustering, and trend tracking.
Businesses can integrate structured news data into ML models, apps, and workflows through a user-friendly API and query builder.
Key Features:
- News API for real-time text analysis
- Advanced sentiment analysis tools
- Entity and concept recognition
- AI-powered categorization models
- Multi-language support
Pros:
✔ Best for media and news monitoring
✔ API-based for easy integration
✔ High accuracy in sentiment detection
Cons:
✖ No drag-and-drop interface
✖ Requires API setup
| Feature | Details |
| Launch Date | 2015 |
| Starting Price | $99/month |
| Capabilities | News analytics, sentiment analysis, entity recognition |
| Public Reception | Great for media monitoring, requires API expertise |
| Ideal Users | Media companies, market researchers |
Conclusion
Text analytics tools have become essential for businesses, researchers, and developers seeking to extract meaningful insights from unstructured text data. From sentiment analysis to entity recognition and predictive modeling, these tools offer a variety of features tailored to different industries and use cases.
FAQs
1. What is text analytics?
Text analytics is the process of using AI and machine learning to analyze and extract insights from unstructured text data, such as customer reviews, social media posts, and emails.
2. Who can benefit from text analytics tools?
Businesses, researchers, data analysts, marketers, developers, and customer service teams can all benefit from text analytics tools for sentiment analysis, topic classification, and decision-making.
3. Are text analytics tools easy to use?
Some tools, like MonkeyLearn and MeaningCloud, offer no-code solutions, while others, like Google Cloud NLP and TextRazor, require coding skills and API integration.
4. How do text analytics tools help businesses?
These tools help businesses analyze customer feedback, detect trends, monitor brand reputation, and automate processes like document classification and fraud detection.
5. What is the best free text analytics tool?
Many tools, including MonkeyLearn, TextRazor, and Google Cloud NLP, offer free plans with limited usage, making them ideal for small-scale projects.
