Natural language processing (NLP) is an exciting branch of artificial intelligence (AI) that allows machines to break down and understand human language. I plan to walk through text pre-processing techniques, machine learning techniques and Python libraries for NLP. Text pre-processing techniques include tokenization, text normalization and data cleaning. Once in a standard format, various machine learning techniques can be applied to better understand the data. This includes using popular modeling techniques to classify emails as spam or not, or to score the sentiment of a tweet on Twitter. Newer, more complex techniques can also be used such as topic modeling, word embeddings or text generation with deep learning. We will walk through an example in Jupyter Notebook that goes through all of the steps of a text analysis project, using several NLP libraries in Python including NLTK, TextBlob, spaCy and gensim along with the standard machine learning libraries including pandas and scikit-learn.

ReviewRealm utilizes NLP techniques like Vader Sentiment Analysis, NLTK, and RAKE to analyze customer reviews and provide valuable insights for smarter purchasing and marketing decisions . The backend is made using python for extra security, Jupyter notebook for creating and sharing computational documents and Mongo for databases(atlas and compass)

This is the code in python (Jupyter Notebook) to perform text analysis to drive sentimental opinion, sentiment scores, readability, passive words, personal pronouns and etc.

The NLP Notebook Repo is a collection of Jupyter notebooks that provide hands-on demonstrations of Natural Language Processing (NLP) techniques. Explore various NLP tasks like text preprocessing, sentiment analysis, named entity recognition, and more. Learn and apply state-of-the-art models and libraries to solve real-world NLP problems.