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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | SOCIAL MEDIA OFFENSIVE CONVERSATION DETECTION USING MACHINE LEARNING |
| Authors Name: | Dhineshkumar T , Preethika S , Prasath M , Sasikala K , Sasikala B |
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IJNRD_210015
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| Published Paper Id: | IJNRD2311388 |
| Published In: | Volume 8 Issue 11, November-2023 |
| DOI: | |
| Abstract: | The proliferation of social media has led to a surge in daily comments, accompanied by a troubling increase in abusive language. This study addresses the urgent issue of cyberbullying through abusive online comments, targeting individuals and groups based on various criteria. Automatic detection of abusive language is crucial for mitigating this problem. Experimental results highlight the superiority of the convolutional neural network (CNN), achieving impressive accuracy rates of 96.2%, 91.4%, and an undisclosed mixed-language dataset. The research emphasizes the effectiveness of one-layer architectures in deep learning models over two-layer architectures. Comparative analysis affirms the significant superiority of deep learning models in detecting and classifying abusive language. In a related context, an application called "Friendly Chat" is introduced to track offensive language in social media chats, fostering respectful interactions. Utilizing a "toxic comment" dataset rated by human critics, the application classifies posts into categories like abuse and hatred. Employing techniques such as Naïve Bayes, LSTM, and Binary relevance, the application detects abusive users in real-time, contributing to a safer online environment. |
| Keywords: | cyberbullying, abusive language detection, convolutional neural network (CNN), comparative analysis, offensive language, Friendly Chat application. |
| Cite Article: | "SOCIAL MEDIA OFFENSIVE CONVERSATION DETECTION USING MACHINE LEARNING", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 11, page no.d799-d804, November-2023, Available :http://www.ijnrd.org/papers/IJNRD2311388.pdf |
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2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2311388 Registration ID: 210015 Published In: Volume 8 Issue 11, November-2023 DOI (Digital Object Identifier): Page No: d799-d804 Country: Dharmapuri, Tamil Nadu, India Research Area: Information Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2311388 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2311388 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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