Toxic Comment Classifier Using NLP and ML

Web App Link :- https://gaurav-van-toxic-comment-web-app-app-24y37c.streamlitapp.com/

Classifying Comments in Six different Categories including their Neutral Cases Using Concepts of NLP and ML

• Toxic
• Severe Toxic
• Threat
• Obscene
• Insult
• Identity Hate

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Concept Used

Instead of Multiclass classification, Binary Classification of Each Category is performed

1. Data Collection - From Kaggle: https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge

2. Data Pre-Procesing - Text Pre-Processing Using Regular Expressions

• Removing \n characters
• Removing Aplha-Numeric Characters
• Removing Punctuations
  
• Removing Non Ascii Characters

3. EDA - Performaing Data analysis to Discover some Issues and trend of the Data

• Through Bar charts of Each Category :- Prob = Class Imbalance -> Solution = Making Frequency of 0s equal to Frequency of 1s by Making Different Dataset of each Category [ id, comment_text, category].
• Helps to solve the Issue of Class Imbalance and Helps in Binary Classification of Each Category

4. Model Building

• VECTORIZATION :- Using TF-IDF and Unigram Approach
• Model Used For Each Category :- KNN, Logistic Regression, SVM, CNB, BNB, DT and RF
• Model Selected - Logistic Regression
• Exporting Trained ML Models as 6 pickle files [ one of each category ]
• Exporting Trained Vectorized Models as 6 pickle files [ one for each category ]
  
  

5. Deployment - Building web app with the help of streamlit and deploying it on Streamlit cloud

Built With

• classification
• data-analysis
• data-science
• jupyter-notebook
• machine-learning
• natural-language-processing
• python
• streamlit
• tokenization