Natural Language Processing Software: Best Tools & Features for 2025

The blog provides an in-depth guide to the best natural language processing (NLP) software for 2025, detailing key features like text analysis, sentiment detection, entity extraction, and multilingual support. It highlights top NLP tools such as Google Cloud Natural Language API, Amazon Comprehend, Microsoft Azure Text Analytics, IBM Watson, and OpenAI models. The article emphasizes the importance of selecting NLP software based on business needs, scalability, integration, and pricing, while also discussing deployment models, security, and customisation options for enterprises.

Teams handle an ever-growing stream of text, from customer emails and chat transcripts to survey comments and support requests. Important insights often sit inside this content, yet manual review slows teams and creates inconsistent decisions.

Natural language processing software and modern NLP tools help solve this challenge by analysing and understanding language at scale. They convert unstructured text into structured insights that product, data, and customer experience teams use for classification, sentiment tracking, summarisation, and better search relevance. Many organisations now rely on AI-based language processing software to support automation and improve user interactions.

The natural language processing market reached $53.42 billion in 2025 and is projected to grow at 24.76% annually through 2031, according to Statista.

This rapid expansion reflects how critical NLP has become for organizations handling text-heavy operations.

This guide explains what natural language processing software does, reviews the best tools in 2025, and gives you a clear framework to choose the right platform for your organisation.

What natural language processing software does?

Natural language processing software analyses and understands human language so machines can process text the way people do. It converts unstructured content into structured information that teams can search, measure, and use for faster decisions. 

These platforms handle tasks such as classifying messages, detecting sentiment, identifying key terms, and producing short summaries. Modern NLP tools apply language models to large volumes of emails, chats, reviews, and documents to deliver consistent results that manual review cannot match. 

Healthcare organizations using NLP have achieved measurable results, with some reporting 40% reductions in documentation time and 50% faster claims handling.

Organisations use this software to automate text-heavy workflows, improve customer interactions, support AI-driven applications, and improve the quality of insights across product, data, and service teams.

Best natural language processing software in 2025

The best natural language processing software in 2025 combines strong language understanding, reliable accuracy, scalable architecture, and practical features that support real business use cases.

1. Google Cloud Natural Language API

Google Cloud Natural Language API widely used natural language processing software built for organisations that need scalable, cloud-native language understanding. It is known for strong entity recognition, reliable sentiment analysis, and broad multilingual support across global datasets. It remains one of the most trusted AI-based language processing software options for enterprise workloads.

Key features 

• Entity Recognition with Google Knowledge Graph: Recognizes complex entities like brands and products with the help of Google’s Knowledge Graph, ensuring more accurate context and entity linking.

• AutoML Text: Customizes models for specific use cases, using Google’s automated optimization system for better performance on your unique datasets.

• Multilingual NLP: Supports multilingual data analysis, making it ideal for global enterprises that need to process and understand text in multiple languages.

• Deep Integration with Google Cloud: Tightly integrated with other Google Cloud services, making it easy to work within the Google ecosystem and automate workflows across your business.

Pros

• Strong multilingual coverage and reliable accuracy

• Mature ecosystem with extensive documentation and support

• Easy integration with Google Cloud storage, databases, and AI services

Cons

• Costs can increase as text volume grows

• Limited flexibility for advanced customisation beyond AutoML

• Requires technical involvement for complex pipelines

Google Cloud Natural Language API works best for teams that want a stable, scalable, and cloud-ready NLP platform backed by enterprise-grade infrastructure. Organisations choose it when they need a proven solution that handles high-volume text, supports multilingual operations, and integrates cleanly with modern data pipelines.

2. Amazon Comprehend

Amazon Comprehend is natural language processing software designed for organisations that work within the AWS ecosystem. It is known for its tight integration with AWS data pipelines and its ability to process large volumes of text with high reliability. It stands out for its domain-adaptive custom models and serverless deployment options built for cloud-native teams.

Key features 

• Custom Classifiers with Comprehend Custom: Create domain-specific classifiers without needing advanced ML expertise, allowing businesses to tailor the tool to their unique needs.

• Event Detection: Identifies actions, relationships, and outcomes across documents, offering contextual insights into key events in your data.

• Built-in PII Detection: Automatically detects personally identifiable information (PII) to help businesses comply with regulatory requirements and ensure data privacy.

• Serverless Deployment: AWS-based solution with serverless deployment options, scaling effortlessly based on text volume.

Pros

• Ideal for teams fully invested in AWS infrastructure

• Strong support for compliance, PII detection, and regulated workloads

• Highly scalable with predictable performance across large datasets

Cons

• Limited flexibility outside AWS environments

• Costs can rise with continuous, high-volume streaming

• Requires engineering support to build complete workflows

Amazon Comprehend works best for data and engineering teams operating in AWS who want reliable NLP capabilities with built-in compliance and seamless data movement. Organisations choose it when they need a cloud-native NLP tool built for scale, automation, and secure enterprise operations.

3. Microsoft Azure Text Analytics

Microsoft Azure Text Analytics is natural language processing software designed for organisations that rely on Microsoft’s cloud ecosystem. It is known for its strong support for enterprise governance and its ability to handle structured and unstructured text together. It stands out for its advanced healthcare-specific models and its deep integration with Azure Cognitive Services.

Key features 

• Healthcare NLP Models: Extracts clinical terms, conditions, medications, and procedures, making it perfect for healthcare-specific applications.

• PII Redaction: Built for compliance-focused organizations, it automatically detects and redacts personally identifiable information (PII) from text.

• Semantic Search Integration: Seamlessly integrates with Azure Cognitive Search to enrich text data and perform semantic search across large datasets.

• Containerized Deployment: Offers containerized deployment options, allowing for secure, on-premise or hybrid cloud deployments that meet strict data governance requirements.

Pros

• Strong governance, security, and compliance support

• Smooth integration with Azure Data Lake, Synapse, and Cognitive Search

• Flexible deployment in cloud, hybrid, or on-prem environments

Cons

• Custom model capabilities are less flexible than Google AutoML or AWS Custom

• Best performance requires familiarity with the Azure ecosystem

• Pricing can become complex across multiple Azure services

Microsoft Azure Text Analytics works best for enterprises that need strict data governance, healthcare-specific NLP, or hybrid deployment options. Organisations choose it when they want natural language processing software that fits tightly into Azure analytics, search, and compliance workflows.

4. IBM Watson Natural Language Understanding

IBM Watson Natural Language Understanding is natural language processing software built for enterprises that need high-governance AI with strong explainability. It is known for its advanced linguistic analysis and its ability to interpret complex, domain-specific text across regulated industries. It stands out for its customisable scoring models and its deep metadata extraction capabilities.

Key features 

• Linguistic Feature Analysis: Analyzes emotions, tone, behavior, and writing style, providing deep insights into the sentiment and intent behind the text.

• Industry Taxonomies: Includes pre-built taxonomies for specialized industries like finance, insurance, and healthcare, enabling more accurate and relevant insights for domain-specific data.

• Metadata Extraction: Extracts valuable metadata, including roles, obligations, risks, and legal contexts, making it ideal for industries with complex documentation.

• Customizable Scoring Models: Provides the ability to create custom scoring models, offering flexibility in how text is interpreted and categorized based on business-specific needs.

Pros

• Strong fit for regulated industries that require transparency and auditing

• Highly customisable scoring and metadata extraction

• Flexible deployment in cloud-restricted or compliance-heavy organisations

Cons

• More complex to configure compared to cloud-native NLP tools

• Less focus on conversational AI compared to modern generative platforms

• Higher cost for full enterprise deployments

IBM Watson Natural Language Understanding works best for large enterprises in finance, insurance, legal, and compliance-focused sectors. Organisations choose it when they need natural language processing software that offers deep linguistic insight, reliable metadata extraction, and deployment models that meet strict security and regulatory requirements.

5. OpenAI language models

OpenAI language models are natural language processing software used by organisations that need advanced language generation and reasoning capabilities. They are known for strong performance in text generation, summarisation, and contextual understanding across varied datasets. OpenAI stands out for its ability to support agentic workflows, conversational interfaces, and multimodal tasks within a single model family.

Key features

• Text Generation with Contextual Understanding: Supports advanced text generation, with the ability to adapt tone, context, and domain for various applications, from chatbots to creative writing.

• Multimodal Processing: Enables processing of both text and images, expanding the range of tasks it can handle, from summarization to content generation.

• Function Calling for Automation: Integrates with systems for controlled automation, allowing users to trigger specific actions based on NLP outputs.

• Embedding Models for Search: Embedding models optimized for semantic search, recommendations, and information retrieval, ensuring faster and more accurate data extraction.

Pros

• Strong performance across generation, reasoning, and long-context tasks

• Fast iteration and model updates that improve accuracy over time

• Large ecosystem of tools, APIs, and developer resources

Cons

• Requires careful usage and guardrails for enterprise governance

• Costs can increase for long-context or high-volume workloads

• Limited offline or on-prem deployment options for restricted environments

OpenAI language models work best for teams that need generative capabilities, semantic search, and conversational AI in one platform. Organisations choose them when they want natural language processing software that excels at reasoning, content generation, and agentic automation while integrating easily into modern AI workflows.

With these unique capabilities in mind, let's now compare the top natural language processing software tools side by side to help you make an informed decision based on features, pricing, and deployment models.

Comparison table of natural language processing software

Below is a comparison table highlighting the key features and differences of the top natural language processing software tools in 2025.

Summary of Key Differences:

• Google Cloud Natural Language API: Ideal for teams needing strong multilingual support and integration with Google Cloud.

• Amazon Comprehend: Perfect for businesses requiring PII detection and custom NLP models within the AWS ecosystem.

• Microsoft Azure Text Analytics: Best for industries in healthcare and regulated sectors with strong governance needs.

• IBM Watson: Suited for enterprises requiring deep linguistic analysis and metadata extraction.

• OpenAI language models: The go-to choice for businesses needing advanced text generation, multimodal processing, and AI-driven conversations.

With an overview of the top NLP tools in 2025, you now have a clear understanding of the features, pricing, and use cases each platform offers. With an overview of the top NLP tools in 2025, you have a solid foundation for understanding your options. However, if you're focused on optimizing conversational data analytics and ensuring data governance, AskEdgi provides a specialized solution built specifically for those needs.

AskEdgi offers a streamlined, turnkey platform for real-time conversational insights and governance, seamlessly integrating into your existing workflows without the complexity of traditional NLP tools.

Key features to expect in natural language processing software

When choosing natural language processing software, there are several key features you should expect to ensure the tool is effective, scalable, and capable of meeting your business needs. The right NLP software should be able to analyse text, extract meaningful insights, and integrate seamlessly into your workflows.

1. Text analysis, sentiment analysis, and entity extraction

• Text analysis helps categorise content, identify topics, and summarise large datasets, ensuring that the tool can handle vast amounts of text data efficiently.

• Sentiment analysis detects emotions and opinions in text, enabling businesses to gauge customer satisfaction and detect potential issues.

• Entity extraction identifies specific entities such as names, brands, and locations, which is critical for tasks like customer feedback analysis and document management.

2. Multilingual support and domain adaptation

• Multilingual support allows businesses to operate across global markets, ensuring the NLP software can handle text in various languages and cultural contexts.

• Domain adaptation tailors models to specific industries, such as healthcare or finance, improving the relevance and accuracy of the results in those areas.

• With the increasing globalisation of businesses, these features ensure that the software can scale and adapt to diverse needs.

3. Custom model training and fine-tuning

• The ability to train and fine-tune models on specific datasets allows businesses to customise NLP models for their unique requirements, whether that’s specialised terminology or specific use cases.

• This feature enables more accurate insights and stronger business outcomes, as the software can be tailored to meet specific business challenges.

• Customisation also makes the software more adaptable, ensuring it can grow with your business.

4. Integrations with data systems and applications

• Seamless integration with existing data systems like CRMs, databases, and customer service platforms ensures that the NLP software can work with your current technology stack.

• This is crucial for ensuring smooth workflows and automating processes, from sentiment analysis to actionable insights.

• A well-integrated system improves efficiency and ensures that the NLP tool adds tangible value to your existing processes.

5. Security, privacy, and compliance controls

• Security features, such as encryption and secure data handling, are critical for businesses that process sensitive customer or financial data.

• Privacy controls and the ability to comply with global data protection regulations, such as GDPR, are essential for ensuring ethical and legal compliance.

• Compliance tools ensure the software meets industry-specific standards, safeguarding your business against potential risks.

6. Performance, scalability, and monitoring

• Performance ensures that the NLP tool processes text at high speed, even when dealing with large volumes of data.

• Scalability allows businesses to expand their operations without sacrificing performance, ensuring that the software can grow with their business.

• Monitoring tools provide insights into how well the NLP models are working, enabling businesses to track performance and make adjustments when necessary.

Cloud deployment holds 63.40% of the NLP market in 2025, with organizations favoring scalable infrastructure that avoids on-premise hardware investments.

Now that we’ve covered the key features of NLP software, let’s look at the evaluation criteria for selecting the best NLP tool for your needs.

Evaluation criteria for selecting natural language processing software

Choosing the right natural language processing software involves evaluating several key factors to ensure it meets your specific business needs. These criteria will help ensure the software is effective, scalable, and integrates seamlessly into your existing workflows.

1. Align features with business use cases

• Ensure the tool supports your specific goals (e.g., sentiment analysis for customer feedback or text generation for content creation).

• Look for customisable models for specialised tasks.

• Choose tools that handle your industry’s unique terminology (e.g., legal, healthcare).

2. Compare accuracy, speed, and language coverage

• Accuracy is crucial for tasks like sentiment detection and entity extraction.

• Check if the software processes data quickly for real-time insights.

• Ensure multilingual support if your business operates globally.

3. Assess usability for technical and non-technical teams

• Look for user-friendly interfaces if your team lacks coding expertise.

• Ensure the software offers customisation options for developers.

• Evaluate integration simplicity with your existing tech stack (e.g., CRM, BI tools).

4. Review pricing models and total cost of ownership

• Compare subscription-based and pay-per-use models to suit your budget.

• Factor in long-term costs for scaling, training, and support.

• Consider the total cost of ownership, including setup and maintenance

Incorporating these evaluation criteria into your decision-making process will ensure that you select the best NLP software for your business needs.

If you're ready to dive deeper or want hands-on support, consider reaching out to our team for a consultation or a custom demo. 

AskEdgi is the analytics solution trusted by leading companies for conversational data access and automated governance.

Conclusion

Choosing the right natural language processing software is essential for improving how your business handles text-based data. With the right features, scalability, and integrations, NLP tools can help automate processes, generate insights, and improve decision-making across your team.

When evaluating NLP software, consider factors such as accuracy, speed, customisation options, and pricing models to find the best fit for your needs. Make sure to choose a tool that not only aligns with your current workflow but also scales as your business grows.

Ready to take the next step? 

OvalEdge offers powerful data analytics solutions that integrate seamlessly into your existing tech stack, empowering your team to unlock valuable insights from data. 

Contact us today for a free consultation and discover how OvalEdge can drive smarter, data-driven decisions across your organisation.

FAQs

1. What is the difference between NLP and machine learning?

While machine learning focuses on finding patterns in numerical or structured data, natural language processing software specialises in understanding, interpreting, and generating human language. NLP relies on ML algorithms but applies them to text, speech, or language, enabling tasks like sentiment analysis, summarization, or entity extraction.

2. Can natural language processing software handle multiple languages?

Yes. Many modern NLP tools support multilingual text processing, enabling analysis, classification, translation, or summarization across languages. This makes them useful for global teams and enterprises dealing with international customer feedback or documents.

3. What are the common limitations of NLP software?

NLP tools may struggle with ambiguous language, slang, idioms, or context‑heavy expressions. Accuracy can drop for domain‑specific jargon, low‑resource languages, or noisy data (typos, mixed content). They may also require careful configuration and validation to avoid misinterpretations.

4. Do non‑technical teams need coding skills to use NLP tools?

Not always. Some NLP tools are built with no‑code interfaces, drag‑and‑drop setups, or self‑service dashboards. These allow non-technical users to perform sentiment analysis, text classification, or summarization without writing code, though advanced customisation may still require technical skills.

5. How secure is text data when using NLP software?

Security depends on the deployment model. Enterprise-grade NLP software offers encryption, compliance controls, and on-premise or private‑cloud deployment, ensuring data stays protected, and privacy standards (like GDPR) are met.

6. Can NLP software be deployed on-premises instead of the cloud?

Yes. Several NLP platforms offer on‑premise or hybrid deployment options. This suits organisations needing strict data control, regulatory compliance, or limited cloud access while still using language processing capabilities.