MonkeyLearn is a user-friendly, NLP-powered platform that helps you gain valuable insights from your text data.
Once you’ve trained your models to deliver accurate insights, you can connect your text analysis models to your favorite apps (like Google Sheets, Zendesk, Excel or Zapier) using our integrations (no coding skills needed!), or through MonkeyLearn’s APIs, available in all major programming languages.
The Natural Language Toolkit (NLTK) with Python is one of the leading tools in NLP model building. Focused on research and education in the NLP field, NLTK is bolstered by an active community, as well as a range of tutorials for language processing, sample datasets, and resources that include a comprehensive Language Processing and Python handbook.
Stanford Core NLP is a popular library built and maintained by the NLP community at Stanford University. It’s written in Java ‒ so you’ll need to install JDK on your computer ‒ but it has APIs in most programming languages.
One of the newest open-source Natural Language Processing with Python libraries on our list is SpaCy. It’s lightning-fast, easy to use, well-documented, and designed to support large volumes of data, not to mention, boasts a series of pretrained NLP models that make your job even easier. Unlike NLTK or CoreNLP, which display a number of algorithms for each task, SpaCy keeps its menu short and serves up the best available option for each task at hand.
Gensim is a highly specialized Python library that largely deals with topic modeling tasks using algorithms like Latent Dirichlet Allocation (LDA). It’s also excellent at recognizing text similarities, indexing texts, and navigating different documents.