This paper presents a survey on hate speech detection using natural language processing (NLP). With the increasing amount of social media content, the volume of online hate speech is also growing. The survey explores key areas of research in automatically recognizing hate speech through NLP, including the limitations of current approaches. The paper discusses various features used in hate speech detection, such as surface-level features, word generalization, sentiment analysis, lexical resources, linguistic features, knowledge-based features, and meta-information. It also covers multimodal information, which includes non-textual content like images and videos. The survey highlights the importance of context in hate speech detection, as well as the challenges in accurately identifying hate speech due to its complex and nuanced nature. The paper also discusses the role of different classification methods, including supervised learning, and the importance of annotated data for training models. It notes that while some approaches have shown promise, there is still a need for more comprehensive and standardized benchmarks for hate speech detection. The survey concludes that hate speech detection remains a challenging task, requiring further research into context-aware methods and the development of more effective features and models.This paper presents a survey on hate speech detection using natural language processing (NLP). With the increasing amount of social media content, the volume of online hate speech is also growing. The survey explores key areas of research in automatically recognizing hate speech through NLP, including the limitations of current approaches. The paper discusses various features used in hate speech detection, such as surface-level features, word generalization, sentiment analysis, lexical resources, linguistic features, knowledge-based features, and meta-information. It also covers multimodal information, which includes non-textual content like images and videos. The survey highlights the importance of context in hate speech detection, as well as the challenges in accurately identifying hate speech due to its complex and nuanced nature. The paper also discusses the role of different classification methods, including supervised learning, and the importance of annotated data for training models. It notes that while some approaches have shown promise, there is still a need for more comprehensive and standardized benchmarks for hate speech detection. The survey concludes that hate speech detection remains a challenging task, requiring further research into context-aware methods and the development of more effective features and models.