Predicting Hate Words and Offensive Language: A Machine Learning Approach

The internet has led to a significant increase in the amount of information available, which has resulted in the rise of online communication among users. Unfortunately, this has also resulted in the proliferation of toxic online texts, which can lead to bullying, harassment, and personal attacks. While there have been attempts to develop efficient models for predicting toxic comments online, the field is still in its nascent stage, hence new frameworks and strategies are needed to address this issue. Convolutional Neural Networks (CNNs) are a promising solution for text classification due to recent advancements in hardware and big data management. Additionally, the increasing amount of data available necessitates the development of novel machine learning tools for managing this information. This study compares CNNs with the traditional bag-of-words approach as well as more efficient methods for text classification. The results demonstrate that CNNs outperform the conventional method in classifying harmful comments, indicating the approach’s potential for further research in this field. This work highlights the importance of developing innovative strategies to combat online toxicity and provides insight into how deep learning techniques can be used for text classification.