Feature Engineering and Hyperparameter Tuning for Identifying Abusive Content Online
Enhancing Text Classification Models to Detect Cyberbullying and Hate Speech Using Feature Engineering Techniques and Support Vector Machines
Feature Engineering and Hyperparameter Tuning for Identifying Abusive Content Online
Embark on a comprehensive exploration of Feature Engineering and Hyperparameter Tuning using Support Vector Machines (SVMs) and Python to detect abusive content online, such as cyberbullying and hate speech. This in-depth project write-up will delve into the following topics:
- Understanding the text classification problem for identifying abusive content online
- Performing Exploratory Data Analysis (EDA) on text data
- Implementing feature extraction and selection techniques, such as Bag-of-Words (BOW) and Term Frequency-Inverse Document Frequency (TF-IDF)
- Preprocessing text data with tokenization, stopwords removal, and stemming
- Building a Support Vector Machine (SVM) model for text classification
- Optimizing model performance through hyperparameter tuning using grid search and random search techniques
Join us as we uncover the potential of feature engineering and hyperparameter tuning in enhancing text classification models for identifying abusive content online. Whether you’re an AI enthusiast or a seasoned data scientist, this project write-up offers valuable insights and in-depth analysis of solving the abusive content detection problem using feature engineering techniques and SVMs.
Abusive Content Detection Problem
The abusive content detection problem involves classifying text data as abusive or non-abusive. This project focuses on understanding the text classification problem, performing Exploratory Data Analysis (EDA), applying feature extraction and selection techniques, preprocessing text data, building an SVM model, and optimizing model performance using hyperparameter tuning.
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Conclusion
In this project, we delved into the abusive content detection problem, performed Exploratory Data Analysis (EDA), applied feature extraction and selection techniques, preprocessed text data, built an SVM model, and optimized model performance using hyperparameter tuning. The optimized model achieved improved accuracy and F1-score on the test data. This project showcases the importance of feature engineering and hyperparameter tuning in enhancing the performance of text classification models for identifying abusive content online.
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