HATE AND OFFENSIVE TEXT DETECTION USING DEEP LEARNING
Enormous, Significance, CNN, LSTM, BERT, Approximately
n recent years, people write and post abusive language on online social media platforms such as Twitter, Facebook, etc which is easily spread on internet. due to enormous volume of such posts the problem of detecting hateful and offensive text in social media is very difficult to solve manually. Hence systems that can automatically detect hate and offensive text in social media has lot of significance in modern world. In this project, Bidirectional Encoder Representation from Transformers (BERT+CNN) , Convolutional Neural Network (CNN), and Linear Short Term Memory (LSTM) are used to identify hateful text. A benchmark dataset of approximately 25 thousand annotated tweets or used to construct models based on deep learning methods. The effectiveness of BERT+CNN , CNN and LSTM models are experimentally analyzed and compared all these models classification performance. The overall aim of these project is to develop an efficient Deep Learning model for detection of hateful and offensive text automatically.
"HATE AND OFFENSIVE TEXT DETECTION USING DEEP LEARNING", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.8, Issue 9, page no.241 - 248, September-2023, Available :https://ijsdr.org/papers/IJSDR2309039.pdf
Volume 8
Issue 9,
September-2023
Pages : 241 - 248
Paper Reg. ID: IJSDR_208496
Published Paper Id: IJSDR2309039
Downloads: 000347162
Research Area: Computer Science & Technology
Country: VISAKHAPATNAM, ANDHRA PRADESH, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
