Solve your natural language processing problems with smart deep neural networks

Predicting Political Ideology of Twitter Users.

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.

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.

Turkcell&Miuul Data Science Bootcamp - Assignments

Using BBC News data to analyze the sentiment of news, from title and description.

All NLP related courses on DataCamp

Hey there! Welcome to NLP project repository. Here i will upload all the NLP based projects i have done or i am currently doing. Feel free to fork this repo and contribute in it.

Unsupervised Machine Learning project for Netflix Movies and TV Shows Clustering. The main goal of this project is to create a content-based recommender system that recommends top 10 shows to users based on their viewing history.

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 V…

NLP using NLTK python library

Implemented Text Summarization by using Text Ranking(simple graph based technique) and Sq2Sq Encoder Decoder Model

Twitter-Sentiment-Analysis-Chandigarh University

Performed PySpark based text pre-processing including lemmatization, POS tagging and UDF functions on customer feedback. Computed and visualized sentiment score to identify areas of improvements.

NLP starter kit

AlmaBetter Capstone Project -Classification model to predict the sentiment of COVID-19 tweets. The tweets have been pulled from Twitter and manual tagging has been done then.

Fuzzy Matcher utility provides you robust fuzzy matching based on Levenstein distance enabled with caching and parallization