A basic machine learning model built in python jupyter notebook to classify whether a set of tweets into two categories: racist/sexist non-racist/sexist.
This notebook contains entire text preprocessing pipeline for NLP problems. The ready-to-use functions require NLTK and SKlearn package installations. It also contains some prominent text classification models.
This is an NLP and Flask-based application which involves predicting the sentiments of the sentences as positive or negative. The classifier is trained on a huge dataset of IMDB movies reviews. The model is then hosted using Flask to be used by end-users.
For the text Mining course I carried out a project related to the analysis and classification of the reviews of the "UCI ML Drug Review" dataset (link: https://archive.ics.uci.edu/ml/datasets/Drug+Review+Dataset+%28Drugs.com%29). I learned to apply techniques such as bag of words, TF-IDF and build sentiment analysis models through the Bert and Vader model.
This repository is for all the method involve in building advance and industrial application of the Natural language processing. This repository is only for learning purposes. This repository is based on the books "Natural Language Processing Recipes" by Akshay Kulkarni, Adarsha Shivananda
The system is implemented to scrape data from a booking website, perform Emotion Analysis on the reviews of the selected hotel and visualized the result over a time axis. R is used to implement the system and Shiny library is used to develop the Front-end.
This notebook contains entire text preprocessing pipeline for NLP problems. The ready-to-use functions require NLTK and SKlearn package installations. It also contains some prominent text classification models.