NLTK Source

Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.

Stock market analyzer and predictor using Elasticsearch, Twitter, News headlines and Python natural language processing and sentiment analysis

Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.

NLTK Data

Learn how to process, classify, cluster, summarize, understand syntax, semantics and sentiment of text data with the power of Python! This repository contains code and datasets used in my book, "Text Analytics with Python" published by Apress/Springer.

Python implementation of the Rapid Automatic Keyword Extraction algorithm using NLTK.

Python AI assistant 🧠

The Classical Language Toolkit

Generates a quiz for a Wikipedia page using parts of speech and text chunking.

A tool to suggest github repositories based on the repositories you have shown interest in.

Creating a software for automatic monitoring in online proctoring

The hands-on NLTK tutorial for NLP in Python

RSS feed aggregator with collections and NLP article summarization

RAG LLM Ops App for easy deployment and testing

Chatbot system for Final Year Project. Chatbot made in Python using Natural Language Toolkit especially Machine Learning. Easy to Understand and Implement.

Machine Learning and NLP: Text Classification using python, scikit-learn and NLTK

keras project that parses and analyze english resumes

Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].

NLP Cheat Sheet, Python, spacy, LexNPL, NLTK, tokenization, stemming, sentence detection, named entity recognition

This repository consists of all my NLP Projects

:video_camera: A tool for automatic video creation and uploading on YouTube

⚡ GUI for editing LLM vector embeddings. No more blind chunking. Upload content in any file extension, join and split chunks, edit metadata and embedding tokens + remove stop-words and punctuation with one click, add images, and download in .veml to share it with your team.

This is a AI Based Smart Exam Proctoring System using python flask, mysql as database, yolov4

Technical and sentiment analysis to predict the stock market with machine learning models based on historical time series data and news article sentiment collected using APIs and web scraping.

텐서플로우와 머신러닝으로 시작하는 자연어처리(로지스틱회귀부터 트랜스포머 챗봇까지)

Transform AI-generated text into formal, human-like, and academic writing with ease, avoids AI detector!

Awesome-Text-Classification Projects,Papers,Tutorial .

Python tutorials as Jupyter Notebooks for NLP, ML, AI

Python package for analyzing Telegram chats and finding correlations between people