8 Best Natural Language Processing Tools 2024
Natural language processing (NLP) tools are software applications that can process and analyze text or speech in standard conversation format. The advantage offered by NLP is that users can interact with artificial systems without needing to input software code or other complex commands. To accomplish this, NLP tools leverage machine learning algorithms, linguistic rules, and […]
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Natural language processing (NLP) tools are software applications that can process and analyze text or speech in standard conversation format. The advantage offered by NLP is that users can interact with artificial systems without needing to input software code or other complex commands.
To accomplish this, NLP tools leverage machine learning algorithms, linguistic rules, and statistical techniques. NLP’s ability to understand human language is enabling AI to advance at an exponentially faster pace. These tools offer significant competitive advantage to those companies that effectively use them.
This review of the best NLP software analyzed the eight top-rated tools for various users and organizations. We evaluated each tool’s features, pros and cons, and pricing. See our top picks below and read to the end to find out which NLP software is best for your business.
- Gensim: Best for Budget-Conscious NLP Projects
- spaCy: Best for Lightning-Fast Text Processing NLP Tasks
- IBM Watson: Best for Advanced Text Analytics
- Natural Language Toolkit: Best for Educators and Researchers
- MonkeyLearn: Best for Ease of Use and Performing Text Analysis
- TextBlob: Best for Initial Prototyping in NLP Projects
- Stanford Core NLP: Best for Executing Complex NLP Tasks
- Google Cloud Natural Language API: Best for Extracting Insights from Text Data
TABLE OF CONTENTS
Top Natural Language Processing Software Comparison
| Language Support | Open Source | License | Cost | |
|---|---|---|---|---|
| Gensim | Python | Yes | LGPL | Free |
| spaCy | Python | Yes | MIT | Free |
| IBM Watson | Various | No | Proprietary | Starts at $0.003 per item |
| Natural Language Toolkit | Python | Yes | Apache 2.0 | Free |
| MonkeyLearn | Web-based API | No | Proprietary | $299/month |
| TextBlob | Python | Yes | MIT | Free |
| Stanford Core NLP | Java | Yes | GPL | Free |
| Google Cloud Natural Language API | Various | No | Proprietary | Varies |
Gensim: Best for Budget-Conscious NLP Projects
Gensim is an open-source Python library – so it can be used free of charge – for natural language processing tasks such as document indexing, similarity retrieval, and unsupervised semantic modeling. It is commonly used for analyzing plain text to uncover the semantic structure within documents. The solution provides algorithms and tools for implementing various machine learning models, such as Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), and word2vec.
Gensim also offers pre-trained models for word embeddings, which can be used for tasks like semantic similarity, document classification, and clustering. Our research found that Gensim can process large text collections using incremental online algorithms without requiring all the text data to be stored in memory, making it suitable for analyzing extensive web-based text datasets.
Pros and Cons
| Pros | Cons |
|---|---|
| Platform independent | Mastering the algorithms and techniques for text analysis may require some learning and experimentation |
| Users say Gensim’s Word2vec, Doc2vec, and LDA tools are valuable for their workflow | Gensim is ideal for NLP tasks but not as versatile for other ML tasks |
Pricing
Gensim is free, open-source software published under the GNU Lesser General Public License (LGPL).
Features
- Data streaming
- Similarity retrieval
- The software runs on Linux, Windows, OS X, and any other platform that supports Python 3.6+ and NumPy
- Gensim allows users to efficiently index and store large collections of text documents for easy retrieval and analysis
spaCy: Best for Advanced NLP Tasks
spaCy stands out for its speed and efficiency in text processing, making it a top choice for large-scale NLP tasks. Its pre-trained models can perform various NLP tasks out of the box, including tokenization, part-of-speech tagging, and dependency parsing. Its ease of use and streamlined API make it a popular choice among developers and researchers working on NLP projects.
SpaCy’s support for over 75 languages and 84 trained pipelines for 25 languages makes it a versatile tool for working with text in different languages. It uses multi-task learning with pre-trained transformers like BERT, allowing users to leverage state-of-the-art models for various NLP tasks.
Pros and Cons
| Pros | Cons |
|---|---|
| Easy to use; spaCy’s API is straightforward and user-friendly | Limited flexibility compared to Natural Language Toolkit (NLTK) in terms of customization and fine-tuning |
| It’s common in the NLP community due to its speed and efficiency | Limited language support |
Pricing
SpaCy is a free, open-source library that can be used for both personal and commercial projects.
Features
- Sentiment analysis
- Dependency parsing
- Word vectors
- Components for named entity recognition, part-of-speech tagging, sentence segmentation, text classification, lemmatization, morphological analysis, and entity linking
IBM Watson: Best for Advanced Text Analytics
IBM Watson Natural Language Understanding (NLU) is a cloud-based platform that uses advanced artificial intelligence and natural language processing techniques to analyze and understand text data. It can extract critical information from unstructured text, such as entities, keywords, sentiment, emotion, and categories. IBM Watson NLU can be used in various applications, such as social media monitoring, customer feedback analysis, and content categorization.
IBM Watson NLU is well-suited for organizations that require advanced text analytics capabilities to enhance their decision-making processes and gain a deeper understanding of customer feedback, market trends, and other textual data sources.
Pros and Cons
| Pros | Cons |
|---|---|
| Simple and easy to use | Documentation can be improved |
| It recognizes semantic roles | Sentiment analysis accuracy can be improved |
Pricing
- Lite: The Lite plan includes 30,000 NLU items and one custom model per month. If you exceed this limit, your NLU instance will be temporarily suspended until the start of the following month.
- Standard: The Standard Plan includes support for over 5 million items per month, with pricing starting at $0.003 per item. This version also offers unlimited custom entities and relations models trained with WKS for $800 and custom classification models for $25.
Features
- Text analytics
- Domain customization
- Entity detection — detects people, places, events, and other entities mentioned in your content
- Sentiment analysis
- Emotion extractions
Natural Language Toolkit: Best for Educators and Researchers
The Natural Language Toolkit (NLTK) is a Python library for NLP tasks. It includes modules for functions such as tokenization, part-of-speech tagging, parsing, and named entity recognition. NLTK also provides access to various corpora (over 50) and lexicons for use in natural language processing projects.
NLTK is widely used in academia and industry for research, education, and NLP application building, and so have major community support. It offers a wide range of functionality for processing and analyzing text data, making it a valuable resource for those working on tasks such as sentiment analysis, text classification, machine translation, and more.
Pros and Cons
| Pros | Cons |
|---|---|
| It offers educational value as it’s widely used by linguists, educators, researchers, and students | It may not always offer the best performance for large-scale production systems compared to more optimized libraries and frameworks |
| Rich community support | NLTK can be complex for beginners |
Pricing
NLTK is a free, open-source, community-driven project.
Features
- NLTK is available for Windows, Mac OS X, and Linux
- It allows you to tokenize and tag some text
- WordNet integration
- Named entity recognition
MonkeyLearn: Best for Ease of Use in Text Analysis
MonkeyLearn is an ML platform that offers a wide range of text analysis tools for businesses and individuals. With MonkeyLearn, users can build, train, and deploy custom text analysis models to extract insights from their data. The platform provides pre-trained models for everyday text analysis tasks such as sentiment analysis, entity recognition, and keyword extraction, as well as the ability to create custom models tailored to specific needs.
Using MonkeyLearn’s APIs, you can integrate MonkeyLearn with various third-party applications, such as Zapier, Excel, and Zendesk, or your platform.
Pros and Cons
| Pros | Cons |
|---|---|
| User-friendly interface with a modern design | Limited integrations |
| Easy to implement | Some users say it’s expensive |
Pricing
Although MonkeyLearn doesn’t publicly advertise its rates on its website, publicly available information shows that the MonkeyLearn API costs $299/month, including 10k queries/month. For your actual rate, contact the company for a custom quote.
Features
- Text classifiers.
- Text extractors.
- Data visualization capability.
- CSAT analysis.
- Pre-built and custom machine learning models.
TextBlob: Best for Initial Prototyping in NLP Projects
TextBlob is a Python library that provides an intuitive interface for performing everyday NLP tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more. It is built on top of NLTK and Pattern, two widely used NLP libraries in Python.
While it is excellent for initial prototyping in NLP projects, its lower performance inherited from NLTK may make it unsuitable for enterprise large-scale production use. On the other hand, its ease of use is certainly a major positive for TextBlob: there is an enormous amount of new AI projects being launched by beginners (or near beginners) so TextBlob’s user base will likely grow.
Pros and Cons
| Pros | Cons |
|---|---|
| Provides language translation and detection | Sentiment analysis capability can be improved |
| Easy to use | Not fast |
Pricing
TextBlob is a free, open-source library in Python.
Features
- Part-of-speech tagging
- Sentiment analysis
- Classification (Naive Bayes, decision tree)
- Tokenization
- Noun phrase extraction
Stanford Core NLP: Best for Information Extraction in NLP Tasks
Stanford CoreNLP is written in Java and can analyze text in various programming languages, meaning it’s available to a wide array of developers. Indeed, it’s a popular choice for developers working on projects that involve complex processing and understanding natural language text.
CoreNLP provides a set of natural language analysis tools that can give detailed information about the text, such as part-of-speech tagging, named entity recognition, sentiment and text analysis, parsing, dependency and constituency parses, and coreference. Its scalability and speed optimization stand out, making it suitable for complex tasks.
Pros and Cons
| Pros | Cons |
|---|---|
| Multilingual | Limited language support – 8 languages only |
| Fast and efficient | Java dependency |
Pricing
Stanford CoreNLP is a free, open-source tool.
Features
- Dependency parsing
- Tokenization
- Part-of-speech tagging
- Sentiment analysis
- Constituency parsing
- Multilingual Support — up to 8 languages
Google Cloud Natural Language API: Best for Extracting Insights from Text Data
Google Cloud Natural Language API is a service provided by Google that helps developers extract insights from unstructured text using machine learning algorithms. The API can analyze text for sentiment, entities, and syntax and categorize content into different categories. It also provides entity recognition, sentiment analysis, content classification, and syntax analysis tools.
It allows users to build custom ML models using AutoML Natural Language. This API leverages Google’s advanced question-answering and language-understanding technology to help with natural language processing tasks.
Pros and Cons
| Pros | Cons |
|---|---|
| Multilingual support | Initial learning curve |
| Classify documents in 700+ predefined categories | It can be expensive for organizations with large volumes of data |
Pricing
Monthly prices per 1,000-character unit:
| Feature | First 5K / month | 5K+ – 1M | 1M+ – 5M | 5M+ |
|---|---|---|---|---|
| Entity analysis | Free | $0.0010 | $0.00050 | $0.000250 |
| Sentiment analysis | Free | $0.0010 | $0.00050 | $0.000250 |
| Syntax analysis | Free | $0.0005 | $0.00025 | $0.000125 |
| Entity sentiment analysis | Free | $0.0020 | $0.00100 | $0.000500 |
Features
- Entity sentiment analysis
- Content classification
- Content targeting and discovery
- Custom content classification
How to Choose the Best Natural Language Processing Software for Your Business
When shopping for the right natural language processing tool for your needs, consider the specific tasks you want to accomplish with the tool, the complexity of the language data you are working with, the accuracy and performance required, and your budget and technical expertise. And of course this includes your future plans for using AI: you’ll need a NLP solution that not only handles current needs but also scales with time.
Key technical features to consider before making a decision include:
- Natural language understanding
- Text processing and analysis
- Machine learning and deep learning techniques
- Multilingual support
- Scalability and performance
- Integration and APIs
- Accuracy and reliability
Frequently Asked Questions (FAQs)
How Do Natural Language Processing Tools Work?
Natural language processing tools use algorithms and linguistic rules to analyze and interpret human language. These tools can extract meanings, sentiments, and patterns from text data and can be used for language translation, chatbots, and text summarization tasks.
What Are the Applications of Natural Language Processing Tools?
- Sentiment analysis
- Information retrieval
- Speech recognition
- Chatbots
- Machine translation
- Text classification
- Text summarization
What Is the Best NLP Software?
There’s no best natural language processing (NLP) software, as the effectiveness of a tool can vary depending on the specific use case and requirements. Our review captured the best tool for different users and use cases. Generally speaking, an enterprise business user will need a far more robust NLP solution than an academic researcher.
Bottom Line: Natural Language Processing Software Drives AI
In recent years (since the end of 2022, precisely), NLP has become a core part of modern AI, machine learning and other business applications – even existing legacy apps are integrating NLP capabilities into their workflows. Incorporating the best NLP software into your workflows will help you maximize several NLP capabilities, including automation, data extraction, and sentiment analysis.
Investing in the best NLP software can help your business streamline processes, gain insights from unstructured data, and improve customer experiences. Take the time to research and evaluate different options to find the right fit for your organization. Ultimately, the success of your AI strategy will greatly depend on your NLP solution.
For a full portrait of the AI vendors serving a wide array of business needs, read our in-depth guide: 150+ Top AI Companies
The post 8 Best Natural Language Processing Tools 2024 appeared first on eWEEK.