bag-of-words
There are 635 repositories under bag-of-words topic.
vzhou842/profanity-check
A fast, robust Python library to check for offensive language in strings.
rmsalinas/fbow
FBOW (Fast Bag of Words) is an extremmely optimized version of the DBow2/DBow3 libraries.
LingDong-/cope
A modern IDE for writing classical Chinese poetry 格律诗编辑程序
sharmaroshan/Twitter-Sentiment-Analysis
It is a Natural Language Processing Problem where Sentiment Analysis is done by Classifying the Positive tweets from negative tweets by machine learning models for classification, text mining, text analysis, data analysis and data visualization
milaan9/Python_Natural_Language_Processing
This repository consists of a complete guide on natural language processing (NLP) in Python where we'll learn various techniques for implementing NLP including parsing & text processing and understand how to use NLP for text feature engineering.
bijoyandas/Hands-On-Natural-Language-Processing-with-Python
This repository is for my students of Udemy. You can find all lecture codes along with mentioned files for reading in here. So, feel free to clone it and if you have any problem just raise a question.
rth/vtext
Simple NLP in Rust with Python bindings
biolab/orange3-text
🍊 :page_facing_up: Text Mining add-on for Orange3
winkjs/wink-nlp-utils
NLP Functions for amplifying negations, managing elisions, creating ngrams, stems, phonetic codes to tokens and more.
johnbumgarner/wordhoard
This Python module can be used to obtain antonyms, synonyms, hypernyms, hyponyms, homophones and definitions.
johndpope/hcn
Hybrid Code Networks https://arxiv.org/abs/1702.03274
shantanu1109/Coursera-DeepLearning.AI-Natural-Language-Processing-Specialization
This Repository Contains Solution to the Assignments of the Natural Language Processing Specialization from Deeplearning.ai on Coursera Taught by Younes Bensouda Mourri, Łukasz Kaiser, Eddy Shyu
gurkandemir/Bag-of-Visual-Words
Bag of visual words (BOVW) is commonly used in image classification. Its concept is adapted from information retrieval and NLP’s bag of words (BOW).
mcabinaya/Digital-Image-Processing
My works for EE 569 - Digital Image Processing - Spring 2018 - Graduate Coursework at USC - Dr. C.-C. Jay Kuo
radi-cho/tfjs-firebase
Train a model with data from Firestore, save it to Cloud Storage and make predictions in Cloud Functions - entirely using NodeJS
dlab-berkeley/Python-Text-Analysis-Fundamentals
D-Lab's 9 hour introduction to text analysis with Python. Learn how to perform bag-of-words, sentiment analysis, topic modeling, word embeddings, and more, using scikit-learn, NLTK, gensim, and spaCy in Python.
lionelmessi6410/Scene-Recognition-with-Bag-of-Words
Solve classical computer vision topic, image recognition, with simplest method, tiny images and KNN(K Nearest Neighbor) classification, and then move forward to the state-of-the-art techniques, bags of quantized local features and linear classifiers learned by SVC(support vector classifier).
ukairia777/bert-topic-models
BERT 기반의 문맥을 반영한 한국어 토픽 모델링 (BERT Contextualized Topic Models)
97k/spam-ham-web-app
A web app that classifies text as a spam or ham. I am using my own ML algorithm in the backend, Code to that can be found under machine_learning_section. For Live Demo: Checkout this link
roshansridhar/Multimodal-Sentiment-Analysis
Engaged in research to help improve to boost text sentiment analysis using facial features from video using machine learning.
donkeyteethUX/abow
Visual bag of words for fast image matching
shrebox/Political-Ideology-Detection-on-Twitter
Predicting Political Ideology of Twitter Users.
winkjs/wink-distance
Distance/Similarity functions for Bag of Words, Strings, Vectors and more.
insikk/bow_image_retrieval
Bag-of-words Image Retrieval
ksdkamesh99/Spam-Classifier
A Natural Language Processing with SMS Data to predict whether the SMS is Spam/Ham with various ML Algorithms like multinomial-naive-bayes,logistic regression,svm,decision trees to compare accuracy and using various data cleaning and processing techniques like PorterStemmer,CountVectorizer,TFIDF Vetorizer,WordnetLemmatizer. It is implemented using LSTM and Word Embeddings to gain accuracy of 97.84%.
tamanna18/ML-NLP-DL
For learning Purposes
BouzCS/Fashion_Recommender_System
Website application for fashion recommendation using machine learning.
DanielJohnBenton/Ngrams.java
:cake: A library for creating n-grams, skip-grams, bag of words, bag of n-grams, bag of skip-grams.
sharmaroshan/Restaurant-Reviews-Analysis
Using Natural Language Processing and Bag of Words for feature extraction for sentiment analysis of the customers visited in the Restaurant and at last using Classification algorithm to separate Positive and Negative Sentiments.
arshren/MachineLearning
Machine Learning documents
HobbySingh/Vision-Based-Fixed-Wing-Landing
This research uses computer vision and machine learning for implementing a fixed-wing-uav detection technique for vision based net landing on moving ships. A rudimentary technique using SIFT descriptors, Bag-of-words and SVM classification was developed during the study.
ksdkamesh99/Phony-News-Classifier
Phony News Classifier is a repository which contains analysis of a natural language processing application i.e fake news classifier with the help of various text preprocessing strategies like bag of words,tfidf vectorizer,lemmatization,Stemming with Naive bayes and other deep learning RNN (LSTM) and maintaining the detailed accuracy below
MastersProject/fast-detection-of-duplicate-bug-report
Machine learning- based solution to the problem of duplicity in the bug reports repository.
shishir349/Analyzing-the-Email-Opening-Rates
Before building an email marketing campaign, it’s important to define your goals so you know if your campaign will be a success. One of the most vital factors to consider is how many people read and engage with your emails. This is a great indicator to show if your efforts and resources are worth the investment.