Abstract: online social networks are growing rapidly in various directions. A number of industries are using these platforms to promote their products and communicate with end clients. On the other hand, some communities are misusing such platforms to promote hate, violence and negative or bully contents. All these happening misbalance the social environment online and offline too. The proposed work offers a research proposal on online hate and bullies content detection by analyzing the text contents of the social media post, message, and blogs. In this context, machine learning techniques and sentiment features are involved as essential tools to deal with bulk amounts of data. This paper first introduces the complexities and needs of cyberhate and bully content problem then a survey is performed over recently available research contents. Further, basic design and functional aspects are provided for proposing a final data model. Finally the conclusion of the proposal offered with the next step of the proposed research work.
Keywords: sentiment analysis, text mining, natural language processing, cyber bulling, hate speech.
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DOI:
10.17148/IJARCCE.2022.11134
