A fast, robust Python library to check for offensive language in strings.

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.

🍊 📄 Text Mining add-on for Orange3

This Python module can be used to obtain antonyms, synonyms, hypernyms, hyponyms, homophones and definitions.

Hybrid Code Networks https://arxiv.org/abs/1702.03274

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).

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).

Predicting Political Ideology of Twitter Users.

Bag-of-words Image Retrieval

A word hashing method based on vectors of letter n-grams. Currently transforms text into sequences of numbers.

Build a classifier to classify transport using sift and svm

Natural Language Processing with Python, Python ile Dogal Dil Isleme

An experiment on text classification entirely based on word frequencies

Source code for the paper "Mining Journals to the Ground: An Exploratory Analysis of Newspaper Articles"

The goal of this project is to design a classifier to use for sentiment analysis of product reviews. Our training set consists of reviews written by Amazon customers for various food products. The reviews, originally given on a 5 point scale, have been adjusted to a +1 or -1 scale, representing a positive or negative review, respectively.

📑 SeSG (Search String Generator): A approach that uses text mining to build search strings for secondary studies.

Automated Content Grading Using Machine Learning

A Naive Bayes classifier to detect clickbait headlines

Sentiment analysis for amazon product reviews using NLTK, Scikit-Learn, and Keras. Using hyperparameter search and LSTM, our best model achieves ~96% accuracy.

SMP_ETST 2018 christmas