Help Twitter Combat Hate Speech Using NLP and Machine Learning

Objectives

• Use NLP and ML to make a model to identify hate speech (racist or sexist tweets) on Twitter
• Use cleaned-up tweets to build a classification model using NLP techniques, cleanup-specific data for all tweets, regularization, and hyperparameter tuning using stratified k-fold and cross validation to get the best model

Prerequisites

• Sklearn: It includes several effective methods of statistical modeling and machine learning, such as classification, regression, clustering, and dimensionality reduction.
• Grid search: It is a procedure that thoroughly explores a manually chosen portion of the targeted algorithm's hyperparameter space.
• Stratified K-fold: This cross validation is an extension of the cross�validation technique used for classification problems.

Dataset Description

Variable - Description
id - Identifier number of the comment
comment_text - The text in the comment
toxic - Status of toxicity with 0 for nontoxic and 1 for toxic

Steps

Task to Perform

1. Load the tweets file using the read_csv function from the Pandas package
2. Upload the tweets into a list for easy text cleanup and manipulation
3. Apply the following steps to clean up the tweets:

• Normalize the casing
• Use regular expressions and remove user handles that begin with @
• Use regular expressions, and remove URLs
• Use TweetTokenizer from NLTK to tokenize the tweets into individual terms
• Remove stop words
• Remove redundant terms like amp and rt
• Remove # from the tweets while retaining the text that follows it Task to Perform

4. Use the cleanup process to remove terms with a length of 1
5. Check the top terms in the tweets:

• First, get all tokenized terms into one list
• Use the counter, and find the 10 most common terms

6. Format the data for predictive modeling:

• Join the tokens back to form strings, which will be required for the vectorizers
• Assign x and y
• Perform train_test_split using sklearn Task to Perform

7. Use TF-IDF values for the terms as a feature to get into a vector space model

• Import TF-IDF vectorizer from sklearn
• Instantiate the model with a maximum of 5000 terms in your vocabulary
• Fit and apply the vector space model on the train set
• Apply the model on the test set

8. Model building: ordinary logistic regression

• Instantiate logistic regression from sklearn with default parameters
• Fit model on the train data
• Make predictions for the train and the test sets Task to Perform

9. Model evaluation: accuracy, recall, and f_1 score

• Report the accuracy of the train set
• Report the recall on the train set: decent, high, or low
• Get the f_1 score on the train set

10. Adjust the class imbalance, if any

• Adjust the appropriate class in the logistic regression model

11. Train the model again with the adjustment and evaluate

• Train the model on the train set
• Evaluate the predictions on the train set: accuracy, recall, and f_1 score Task to Perform

12. Regularization and hyperparameter tuning:

• Import GridSearch and StratifiedKFold
• Choose for C and penalty parameters under the parameters grid
• Use a balanced class weight while instantiating the logistic regression

13. Find the parameters with the best recall in cross-validation

• Choose recall as the metric for scoring
• Choose a stratified four-fold cross-validation scheme
• Fit it on the train set

14. List the best parameters Task to Perform
15. Predict and evaluate parameters using the best estimator

• Use the best estimator from the grid search to make predictions on the test set
• Find the recall on the test set for the toxic comments
• Find the f_1 score

Setup and Installation:

pip install --upgrade pip
pip install -r requirements.txt
pip list