In modern text data analysis, NLP tools and NLP libraries are indispensable. Researchers and businesses use natural language processing tools to draw information from text data analysis. This analysis includes analyzing customer feedback, automating support systems, improving search and recommendation algorithms, and monitoring social media.

There are a wide array of NLP tools and services available, and knowing their features is key to good results. While some tools are perfect for small projects, others are better for experts working on big data. It all depends on the project.

To help you find the perfect solution for your project, we’ve compiled a list of the best NLP tools, libraries, and services. Below you’ll find free and open-source libraries, crowdsourcing solutions, and specialized annotation companies.

Free NLP Tools

Spark NLP: Considered by many as one of the most widely used NLP libraries, NLP Spark is 100% open source, scalable, and includes full support for Python, Scala, and Java. It offers a whole host of NLP features, pre-trained models and pipelines in multiple languages, and an active community on Slack for discussing implementation and troubleshooting.

NLTK: The Natural Language Toolkit is a platform for building Python programmes to work with human language data. This includes lexical analysis, named entity recognition, tokenization, PoS tagging, parsing, and semantic reasoning. It also offers some great starter resources. However, because NLTK is resource heavy when dealing with big data, it is recommended for simple projects.

PyTorch-Transformers: This NLP library contains pre-trained models for NLP. It features PyTorch implementations, pre-trained model weights, usage scripts, and conversion utilities for models including BERT, GPT-2, Transformer-XL, and RoBERTa.

TextBlob: Built on the shoulders of NLTK, TextBlob is like an extension that simplifies many of NLTK’s functions. It offers an easy to understand interface for tasks including sentiment analysis, PoS tagging, and noun phrase extraction. TextBlob is a recommended natural language processing tool for beginners that is also scalable.

SpaCy: SpaCy is a smooth, fast, and efficient open-source library written in Cython. It features a simple API, pre-trained word vectors, 23 statistical models for 11 languages, built-in visualizers for syntax and NER, and support for more than 53 languages. Its update schedule is also very consistent.

Stanford CoreNLP: CoreNLP is used to apply linguistic analysis to pieces of text. It offers support in 7 languages, and its scalability makes it a good natural language processing tool for information scraping, chatbot training, and text processing & generation. That said, it is licensed under the GNU General Public License v3, so a commercial license is necessary when building any proprietary software.

Apache OpenNLP: This Java-written NLP library is well regarded for its simplicity. It includes tokenization, sentence segmentation, PoS tagging, chunking, parsing, and perceptron-based machine learning. However, Apache is a volunteer-developed project, so the update schedule is erratic.

AllenNLP: An Apache 2.0 research library built on PyTorch, Allen NLP is for researchers who want to build language analysis models quickly and simply. Featuring a wide range of text analysis options, AllenNLP is a simple NLP tool that is also scalable.

GenSim: A free Python library for natural language processing, GenSim is a recommended option for topic modeling and document similarity comparison. Furthermore, it also offers scalable statistical semantics and semantic structure analysis. GenSim boasts high-level processing speed and the ability to handle large amounts of text.

NLP Architect: Developed by the Intel AI Lab, NLP architect is an open source Python Library for optimizing NLP and exploring deep learning topologies. It is designed to make training and running models a simple process.

The above options are great for hobbyists, data researchers, and teams that have the time to perform annotation tasks internally. However, if you have a tight project timeline and big data to process, it might be simpler and more efficient to enlist the help of a qualified NLP service.

Below we’ve compiled a list of four NLP services to help with your data analysis needs. Between them you’ll find customizable timelines, project management assistance, access to professional annotators, and quality assurance guarantees.

NLP Services

Lionbridge: A leading provider of training data and data annotation, Lionbridge utilizes a workforce of 500,000 crowdsourced professionals capable of working in 300+ languages. Their custom annotation platform makes data easy to analyze for a diverse range of use cases, and special project requirements can be easily accommodated. Lionbridge is a good option for high-quality data annotation quickly and at scale.

Amazon Mechanical Turk: The AMT crowd is a cheap, scalable NLP solution for data collection and data labeling. Because they don’t offer project management, quality assurance, or custom invoicing, they’re a good service for projects where these factors aren’t a necessity.

Figure Eight: Now a subsidiary of Appen, Figure Eight provides a machine learning-assisted data annotation platform capable of handling a variety of NLP services. Figure Eight is good for creating unique project ontologies.

Scale: Scale offers NLP data annotation services including entity annotation, OCR transcription, text categorization, and sentiment analysis. By combining human and machine learning annotation practices, their categorization and content moderation services are scalable.

Still not sure how to implement a text data analysis solution? Lionbridge can help you define your project goals, then build and annotate a custom dataset for your specific needs. Contact us to get a clearer picture of how high-quality data can transform your text data analysis projects.