Solve your natural language processing problems with smart deep neural networks
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Updated
Jun 3, 2019 - Jupyter Notebook
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
All NLP related courses on DataCamp
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
NLP starter kit
Twitter-Sentiment-Analysis-Chandigarh University
A novel approach towards video-ranking using intent and relevance feedback
Machine learning model to predict emotions throught text
Implemented Text Summarization by using Text Ranking(simple graph based technique) and Sq2Sq Encoder Decoder Model
Fuzzy Matcher utility provides you robust fuzzy matching based on Levenstein distance enabled with caching and parallization
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.
SpamGuard is an intelligent SMS filtering system designed to detect and filter spam messages using machine learning techniques.
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.
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