A survey on hate speech detection and sentiment analysis using machine learning and deep learning models

Abstract

In today's digital era, the rise of hate speech has emerged as a critical concern, driven by the rapid information-sharing capabilities of social media platforms and online communities. As the internet expands, the proliferation of harmful content, including hate speech, presents considerable obstacles in ensuring a secure and inclusive online environment. In response to this challenge, researchers have embraced machine learning and deep learning methods to create automated systems that can effectively detect hate speech and conduct sentiment analysis, offering potential solutions to address this pressing issue. This survey article provides a comprehensive overview of recent advancements in hate speech detection and sentiment analysis using machine learning and deep learning models. We present an in-depth analysis of various methodologies and datasets employed in this domain. Additionally, we explore the unique challenges faced by these models in accurately identifying and classifying hate speech and sentiment in online text. Finally, we outline areas where more study is needed and suggest potential new avenues for exploration in the field of hate speech identification and sentiment analysis. Using the results of this survey, we hope to encourage the development of more effective machine learning and deep learning-based solutions to curb hate speech and promote a more inclusive online environment.

Keywords

• Hate speech detection
• Sentiment analysis
• Machine learning
• Deep learning
• Inclusive online