Comment-Classifier

A machine learning system which takes a comment as an input and ranks it as offensive or non-offensive (neutral). To measure its effectiveness, the following classification algorithms were used: Naive Bayes, SVM and Random Forest.

Libraries

1. NumPy
2. Pandas
3. scikit-learn

Input

A data set with 6182 comments, extracted from mulitple online platforms like Youtube, Twitter etc...

Procedure

• Preprocess and Text-Cleaning.

• Split data (train & test).

• Train data with the procedure of k-fold cross validation, under the following classification algorithms:

  1. Naive Bayes
  2. SVM
  3. Random Forrest

• Use test data to evaluate algorithms.

• Experimented with TF-IDF (term frequency-inverse document frequency) and POS (parts of speech).

• Improvements

  1. Lemmatization
  2. Stopwords
  3. Bigrams
  4. Laplace Smoothing