A python chatbot framework with Natural Language Understanding and Artificial Intelligence.

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

Crowd-sourced stock analyzer and predictor using Elasticsearch, Twitter, News headlines and Python natural language processing and sentiment analysis

The hands-on NLTK tutorial for NLP in Python

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

keras project that parses and analyze english resumes

Awesome-Text-Classification Projects,Papers,Tutorial .

텐서플로우와 머신러닝으로 시작하는 자연어처리(로지스틱회귀부터 트랜스포머 챗봇까지)

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].

Ruby port of the NLTK Punkt sentence segmentation algorithm

:eyeglasses: Platform to automatically detect what user might be interested in buying in near future

🍊 :page_facing_up: Text Mining add-on for Orange3

:book: [译] Python 自然语言处理 中文第二版

Twitter Sentiment Analysis For Turkish Language

Named entity extraction from Portuguese web text

Sentiment Analysis on the First Republic Party debate in 2016 based on Python,NLTK and ML.

Java port of Python NLTK Vader Sentiment Analyzer. VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on texts from other domains.

Automatic categorization of documents, consists in assigning a category to a text based on the information it contains. We'll follow different approach of Supervised Machine Learning.

Sentiment Analysis of news on stock prices

A company/project name generator for Python. Uses NLTK and diverse techniques derived from existing corporate etymologies and naming agencies for sophisticated word generation and ideation.

This repository provides everything to get started with Python for Text Mining / Natural Language Processing (NLP)

(UNMAINTAINED)Fetch comments from the given video and determine sentiment towards the video is positive or negative