Spam Text Classification

• Spam Text Classification using Machine Learning Models

Multi-Label Text Classification

• stackoverflow.csv dataset is used, which can be found via link: https://raw.githubusercontent.com/laxmimerit/All-CSV-ML-Data-Files-Download/master/stackoverflow.csv
• Tf-idf and MultiLabelBinarizer are used together to prepare train and test dataset from csv file.
• Since Logistic Regression and SVM don't support multi-class classification, OneVsRestClassifier is used.
• The One-vs-Rest strategy splits a multi-class classification into one binary classification problem per class.

Hate Speech Classification

• In Tensorflow, Convolutional Neural Network is used to perform hate speech classification.
• Dataset hate_speech_data.csv can be reached via https://raw.githubusercontent.com/laxmimerit/hate_speech_dataset/master/data.csv.
• First data preprocessing is realized. After that, since the dataset is not huge, only one layer CNN model is built.
• Using from tensorflow.keras.preprocessing.text import Tokenizer, numerical values are assigned to words in dictionary format so that each word has its own word_index.
• Each sentence will be converted into a sequence where each word is replaced by its number in the word index using tokenizer.texts_to_sequences(sentences).