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Classification of Cyber Bulling using NLP/Model/README.md

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 # Cyberbullying, a form of online harassment, has become a pervasive issue in today's digital age. Natural Language Processing (NLP) offers a powerful toolset for addressing and classifying instances of cyberbullying, enhancing our ability to detect and mitigate this harmful behavior.
 
-# Key features:
+-> Key features:
 
-# Text Analysis: NLP techniques enable the analysis of textual content in various online platforms such as social media, chat rooms, and forums. By examining the language used in these interactions, NLP algorithms can identify patterns associated with cyberbullying, including offensive language, threats, and personal attacks.
+-> Text Analysis: NLP techniques enable the analysis of textual content in various online platforms such as social media, chat rooms, and forums. By examining the language used in these interactions, NLP algorithms can identify patterns associated with cyberbullying, including offensive language, threats, and personal attacks.
 
-# Sentiment Analysis: Sentiment analysis, a subset of NLP, aids in discerning the emotional tone of messages. Cyberbullying often involves negative sentiments, and sentiment analysis can help classify messages as potentially harmful. Identifying aggressive or harmful sentiment is crucial in flagging instances of cyberbullying.
+-> Sentiment Analysis: Sentiment analysis, a subset of NLP, aids in discerning the emotional tone of messages. Cyberbullying often involves negative sentiments, and sentiment analysis can help classify messages as potentially harmful. Identifying aggressive or harmful sentiment is crucial in flagging instances of cyberbullying.
 
-# Keyword Extraction: NLP models can extract keywords related to cyberbullying, including derogatory terms, slurs, and threatening language. Keyword extraction facilitates the identification of potentially harmful content, contributing to the classification process.
+-> Keyword Extraction: NLP models can extract keywords related to cyberbullying, including derogatory terms, slurs, and threatening language. Keyword extraction facilitates the identification of potentially harmful content, contributing to the classification process.
 
-# Contextual Understanding: Understanding the context of a message is crucial for accurate classification. NLP models can be trained to consider contextual nuances, distinguishing between playful banter and genuine threats. This contextual understanding enhances the precision of cyberbullying classification.
+-> Contextual Understanding: Understanding the context of a message is crucial for accurate classification. NLP models can be trained to consider contextual nuances, distinguishing between playful banter and genuine threats. This contextual understanding enhances the precision of cyberbullying classification.
 
-Machine Learning Models: Utilizing machine learning algorithms within the NLP framework allows for the development of robust classification models. By training on labeled datasets that include examples of cyberbullying, these models can learn to recognize and classify new instances of such behavior.
+-> Machine Learning Models: Utilizing machine learning algorithms within the NLP framework allows for the development of robust classification models. By training on labeled datasets that include examples of cyberbullying, these models can learn to recognize and classify new instances of such behavior.
 
-Conclusion: In conclusion, the application of NLP in the classification of cyberbullying represents a significant step forward in addressing the challenges posed by online harassment. By leveraging the capabilities of NLP, we can develop more sophisticated and adaptive tools to create safer digital spaces.
+# Conclusion: In conclusion, the application of NLP in the classification of cyberbullying represents a significant step forward in addressing the challenges posed by online harassment. By leveraging the capabilities of NLP, we can develop more sophisticated and adaptive tools to create safer digital spaces.