Data Science Project | Hate Speech Classification
Welcome to our Data Science Project series!
In this session, we embark on a crucial mission to classify hate speech using machine learning techniques. Hate speech classification is essential for maintaining healthy online communities and preventing the spread of harmful content. Join us as we explore the steps to build an effective hate speech classifier.
What You'll Learn:
Introduction to Hate Speech Classification: Understand the significance of identifying and classifying hate speech in online platforms to ensure safe and respectful communication.
-Dataset Overview: Explore the datasets commonly used for hate speech detection, including sources, structure, and annotation processes.
-Data Preprocessing: Step-by-step guide on preparing text data for classification, including text cleaning, tokenization, and handling imbalanced datasets.
-Exploratory Data Analysis (EDA): Techniques for visualizing and understanding the distribution of hate speech in your dataset.
Building a Baseline Model: Learn how to build a baseline machine learning model for hate speech classification using algorithms like Logistic Regression or
Naive Bayes.
Model Evaluation: Introduction to evaluating model performance using metrics such as accuracy, precision, recall, and F1 score.
Why Watch This session ?
This session is essential for data scientists and NLP practitioners interested in tackling the challenging problem of hate speech detection. By the end of this session you'll have a strong foundation in data preprocessing, building baseline models, and evaluating their performance.
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