The dataset used for this project consists of Tweets labeled as hate_speech, offensive_language, or neither. A more comprehensive description of the dataset is provided in initial datasets directory. The accompanying Python 3 scripts make use of Natural Language Processing (NLP) and Machine Learning techniques to engineer various feature spaces and train models to classify a tweet according to one of the three possible labels. In Particular, NLP methods are used to create feature spaces including weighted TF-IDF scores, TF-IDF matrix, N-grams, sentiment scores, and dependency-based features. An in-depth description of each of these feature spaces is included in the README.md found in the feature engineering scripts directory. Subsequently, various multi-class classification algorithms were trained on these feature spaces including Multinomial Logistic Regression, Random Forest, XGBoost, and Multi-layer Perceptron, as well as Ensemble methods including weighted-average voting and stacking with a Logistic Regression meta-classifier.

To identify the most appropriate algorithm for this text classification task, model performance evaluation methods are provided and a comparative analysis between the models discussed above is included. Given the multi-class nature and class label imbalance, micro-average F1-scores and micro-average area under the ROC curve (AUC) are the chosen comparison and evaluation metrics.

NOTE: This is meant to be a customizable model that can be applied to new data, with the option to include any or all of the feature spaces engineered. To exclude a specific feature type, simply comment it out in lines 14-20 of hate_speech_detection.py.

The following describes how to run the hate-speech and offensive language detection model (described above) from scratch including all pre-processing and feature engineering steps:

• STEP 1: Import the .csv file labeled_data.csv (found in the initial datasets directory)

• STEP 2: Open the feature engineering scripts directory and run clean_tweets.py which performs initial text corpus pre-processing. The output of this script is cleaned_tweets.csv which can also be found in the initial datasets directory.

• STEP 3: Open and run each of the remaining scripts in the feature engineering scripts directory which will create each of the feature subsets and output them as .csv files. The .csv file outputs from this step can be found in the feature datasets directory. The dictionaries required for this step can be found in the dictionaries directory.

• STEP 4: Open and run the script hate_speech_detection.py which reads in the .csv files in the feature datasets directory, merges them into a single pandas data frame, trains models to classify instances as either hate speech, offensive language, or neither, and performs model evaluation assessments on the testing set.

• STEP 5: Re-run steps 2-3 on any new raw text data, along with the best performing model trained in step 4, to detect instances of hate-speech and offensive language.

The following Python 3 packages require installation to run this project:

• nltk
• sklearn
• stanfordcorenlp
• pandas
• numpy
• mlxtend.classifier
• re
• string
• json