Applying natural language processing to understanding the latest sentiment of news articles featuring Bitcoin and Ethereum.
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Updated
Aug 8, 2020 - Jupyter Notebook
Applying natural language processing to understanding the latest sentiment of news articles featuring Bitcoin and Ethereum.
I developed a Flask application using NLTK and Docker. Within the application, I created a JSON file named intents, which contains various questions and answers. The Chatbot in the application responds to user queries using the data provided in the intents file.
Data Science Project for Udacity's Data Scientist Program. Here I created a webapp and ML/NLP pipeline that analyzes message data for disaster response and shows classification results.
Here are some of my projects implemented during the training in Yandex.Practicum
ML model for rating prediction with NTLK and Scikit-learn.
Master's Thesis in Natural Language Generation
AutoSubFinder is an innovative coding project that aims to automatically find and download subtitle track files for movies and other videos. The project utilizes web scraping and Natural Language Processing (NLP) techniques to search various subtitle databases and provide accurate and synchronized subtitle tracks for videos.
An implementation of an Information Retrieval System that indexes documents and matches queries using vector space models (Jaccard, Cosine, Scalar), boolean model, and probabilistic model (BM25).
This project is part of Udacity's Data Scientist Nanodegree. In it, we use natural language processing (NLP) to analyze messages from real-world disasters for categorization.
Introduction to Artificial Intelligence with Python
Using text analytics to understand cultural patterns in philosophical texts. Exploring gender, author, region, and time-period differences, and extracting key philosophical concepts.
Employs the NLTK library to craft Shakespearean Sonnets
A python program that recommends the best posts via a rating system developed by me.
The Text Summarizer Web Application utilizes Python, Flask, NLTK, and Bootstrap for creating concise summaries from long texts. No frills, just an efficient tool for simplifying complex content.
For this project, machine learning algorithms are used on amazon fine food reviews dataset to analyze if the given review is a positive review or a negative review.
A simple search engine built using Python 3.11 that implements TF-IDF weighting, page ranking, and cosine vector similarity, and utilizes NLTK libraries for tokenization and stemming.
Web scraping product reviews data from AliExpress.
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