Top 5 Natural Language Processing Libraries for Data Scientist
In this blog post we are going to talk about Natural Language Processing (NLP) which is one of the branches of machine learning which focuses on teaching machines to understand human language. it has multiple applications, from chatbots to sentiment analysis, and is an important skill in the data scientist’s toolbox. let’s look at five of the best natural language libraries for data scientists.
1 – Natural Language Toolkit (NLTK):
NLTK is a Python library for NLP. It has a collection of libraries and programs for symbolic and statistical natural language processing, making it a popular choice for both beginners and experts.
2 – Stanford CoreNLP:
Stanford CoreNLP is a Java-based NLP toolkit. It provides a set of human language technology tools that can analyze text and extract different information from it.
3 – spaCy:
spaCy is a free, open-source NLP library written in Python. It’s built specifically for production, which means it’s fast, efficient and scalable. It provides pre-trained models for various NLP tasks such as named entity recognition, POS tagging and dependency analysis.
4 – Gensim:
Gensim is an open source Python library for topic modeling and vector space modeling. It provides algorithms for NLP tasks such as topic modeling, document similarity analysis, and text summarization.
5 – Apache OpenNLP:
Apache OpenNLP is a machine learning toolkit for NLP tasks. It includes a set of tools for tokenization, sentence segmentation, POS tagging and named entity recognition. These are the useful libraries which allow data scientists to perform a variety of NLP tasks. For example, they can create chatbots, perform social media sentiment analysis, and extract important information from large amounts of text.