NLTK Source

Using Text Mining and Natural Language Processing Techniques pre- processed 50k tweets. Visualized the impact of hashtags on tweets sentiment using Seaborn. Applied machine learning models, calculated f1_scores, accordingly used the best model for sentiment prediction.

Sentiment Analysis Source Code

Lens for E-Learning is an application that surfs the internet for personalized educational content for the users.

This repository contains my course work on exercises and assignments for Natural Language Processing Specialization, which was provided by deeplearning.ai on Coursera

Text Summarization is a very useful technique to get important parts of a large text document. This project uses word frequencies of the sentence and yield a score for each sentence.

Movie Recommendation System It is collaboration based recommendation system in which we search movie and application give us a relevant featured movie which is similar as input movie

sentiment analysis is done using python language and its libraries

This project is part of the Inclass Kaggle competition. The goal is to classify the sentiment polarity ( -1 = negative, 0 = neutral, 1 = positive) for the Airline tweets data.

Implement and Evaluate Perceptrons for text classification

Implement and Evaluate Naive Bayes for text classification

The project is a Python implementation of a Text Summarizer. It uses various natural language processing (NLP) techniques to generate a summary of a given text.

Statistical Analysis of text corpus using python NLTK

Defining the author of the text using Machine learning and NLP.

Python Visualization notebook using data from US Election 2020 Tweets ·