There are 3 repositories under naturallanguageprocessing topic.
A comprehensive reference for all topics related to Natural Language Processing
Projects and useful articles / links
Awesome list (courses, books, videos etc.) and implementation of Machine Learning Algorithms
Python library for feature selection for text features. It has filter method, genetic algorithm and TextFeatureSelectionEnsemble for improving text classification models. Helps improve your machine learning models
An attempt to predict next day's stock price movements using sentiments in tweets with cashtags. Six different ML algorithms were deployed (LogReg, KNN, SVM etc.). Main libraries used: Pandas & Numpy
Using Spacy and NLTK module with Tf-Idf algorithm for text-summarisation. This code will give you the summary of inputted article. You can input text directly or from .txt file, .pdf file or from wikipedia url.
This repo contains my work & The code base for this TensorFlow Developer specialization offered by deeplearning.AI
Document based ChatGPT
Machine translation using the seq2seq model
This is a Flask backend that allows users to upload a PDF file and receive a simplified and humorous explanation of its contents using OpenAI's GPT-3 API. The application uses NLTK to split the PDF text into smaller chunks to stay within the API's maximum token limit, and PyPDF2 to extract the text from the PDF file.
In this project our goal is to acheive the problem of converting audio data into textual data.
How to build a chatbot to parse conditional statements
BookGPTs: Revolutionizing Book Interactions with AI. Create GPTs for any e-book, making technology accessible for all to engage in rich, AI-powered book discussions. No technical expertise required – upload a document and bring your favorite books to conversational life!
A prototype legal text search engine that uses a semantic search algorithm in order to find related keywords and sort the results by relevance.
Various experiments in parsing and natural language processing in the Forth language for the Apple II
This project was done as a part of my internship at Bennet University for in May-June 2020.
Sentiment Analysis of Tweets for a renowned shoe brand
WebScrapeSummarizer 🌐✍️: A web tool that fetches and summarizes content from any domain, offering insights in a compact CSV format.
This repository contains code for generating blog content using the LLama 2 language model. It integrates with Streamlit for easy user interaction. Simply input your blog topic, desired word count, and writing style to generate engaging blog content.
A software designed to read messages to make it convenient for people to go through big messages and understand them easily in an efficient manner.
Sentiment Analysis on Twitter Data . Classifying them based on polarity into positive, negative and neutral Using Classical Machine Learning methods.
The Harmony project
Applications of Embeddings, Machine Translation and Spam Text Classification.
School assignment for text analytics
This project provides a simple script for determining the sentiment of a text input using TextBlob library in Python. It also returns the most positive and most negative sentence in the input text. The script can be used as a standalone tool or integrated into other projects.
Using Gensim and SpaCy models for topic modeling in the news, and experimenting with LTSMs and GRUs to explore features such as writing style and sentiment per news category
A project consisting of analysis of sarcasm in text using Natural Language Processing techniques. It highlights the importance of context and punctuation in sarcasm detection. Different deep learning models are applied and compared to get the best accuracy in sarcasm detection.
"Detect sarcasm effortlessly! This Python app uses NLP and ML to analyze text sentiment, distinguishing sarcastic tones. With a user-friendly interface, input any text for real-time sarcasm identification. Achieve accurate results through advanced sentiment analysis techniques and trained models."
python class for literature training from biomedical literature. It reads the text from the pdf and then implements the tokens and then uses the BERT model to train the model
A sentiment analysis in Hebrew based on HeBert (https://github.com/avichaychriqui/HeBERT) of WhatsApp groups!