Abstract: The detection of hate speech in social media is a crucial task. The uncontrolled spread of hate has the potential to gravely damage our society, and severely harm marginalized people or groups. Hate speechis one of the most dangerous of these activities, so users have to protect themselves from these activities from YouTube, Facebook, and Twitter etc.To identify word similarities in the tweets made by bullies and make use of machine learning and can develop an ML model automatically detect social media bullying actions. However, many social media bullying detection techniques have been implemented, but many of them were textual based. The objective of our project work is to show the implementation of NLP and CNN which detects bullied tweets, posts, etc. A machine learning model is proposed to detect and prevent bullying on Twitter. Two classifiers i.e. NLP(Natural Language Processing) are used for identifying the complete sentence in the comments and CNN(Convolution Neural Networks) for image identification. Both NLP and CNN were able to detect the true positives with more accuracy. Also, Twitter API is used to fetch tweets and tweets are passed to the model to detect whether the tweets are bullying or not.
Keywords: Natural Language Processing(NLP),Long Short Term Memory(LSTM)
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DOI:
10.17148/IJARCCE.2024.13518
