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One of the most popular communication and information sharing media has been the social media. Facebook is one of the platforms that produce a colossal user-formed textual information in the form of posts, comments, and messages. Though it is true that these platforms help in social interaction as well as exchange of knowledge, they also make possible the propagation of illicit content like hate speech, abusive language and offensive words. Social media hate speech has the potential to harm individuals, society, and social balance as a whole, thus its identification can be a relevant research issue. This study suggests a model of identifying hate speech using unstructured Facebook data on the basis of sentiment and emotion analysis and natural language processing methods. The suggested framework evaluates the user-generated content by utilizing a series of steps such as data collection, preprocessing, feature extraction, and classification. Sentiment analysis is applied to determine the polarity of a text, whereas emotion analysis identifies the emotional state of anger, fear, disgust, and sadness, which are typically related to hateful communication. These characteristics are coupled with machine-learning based text classification models that classify the social media text to either hate speech, offensive speech, or non-hate speech. The suggested system will enhance the precision of harmful communication detection through a combination of the emotional and sentiment-based features with the contextual language understanding. The framework will help social media sites to monitor the online interactions and detect abusive content automatically with a higher degree of effectiveness. Finally, this strategy will help create safer and more responsible digital spaces by aiding in the identification and detection of hate speech in massive social media information.