Sentiment Analysis and Deep Learning Based Cyber Bullying Detection in Twitter Dataset 
Sherly T.T1, B. Rosiline Jeetha2
1Sherly T.T*, Research Scholar, PG and Research Department of Computer Science, Dr. N.G.P. Arts and Science College, Coimbatore, Tamil Nadu, India.
2B. Rosiline Jeetha, Research Guide, PG and Research Department of Computer Science, Dr. N.G.P. Arts and Science College, Coimbatore, Tamil Nadu, India.
Manuscript received on September 27, 2021. | Revised Manuscript received on October 27, 2021. | Manuscript published on November 30, 2021. | PP: 15-25 | Volume-10 Issue-4, November 2021. | Retrieval Number: 100.1/ijrte.D65111110421 | DOI: 10.35940/ijrte.D6511.1110421
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: When somebody, usually a teenager, abuses or harasses individual on the internet and other digital places, mainly on social networking platforms, this is termed as cyberbullying. Cyberbullying, like all types of bullying, produces psychological, emotional, and physical distress. Every individual’s reaction to being bullied is diverse, but research has discovered certain common patterns. In a recent study, we introduced a technique called Hybrid Firefly Artificial Neural Networks (HFANN) to combat cyberbullying. Nevertheless, without considering the sentiment analysis features, accuracy of cyber bullying identification is lowered in this study. The Sentiment Analysis and Deep Learning based Cyber Bullying Detection (SADL-CDD) approach is used in the suggested research approach to address this issue. The punctuations, urls, html tags, and emoticons from the input tweet comments are removed first in this study project. Sentiment feature extraction is performed after pre-processing to improve classification accuracy. The Modified Fruit Fly Algorithm (MFFA) is used to choose the best features from the extracted features. Following feature selection, cyber bullying detection is carried out using a Hybrid Recurrent Residual Convolutional Neural Network (HRecRCNN). The experimental outcome of this study indicates the efficiency of the suggested approach. In comparison to current algorithms, the SADL-CDD method delivers improved classification performance with respect to reduced time complexity, greater precision, recall, f-measure, and accuracy.
Keywords: Cyber bullying, Artificial neural network, Deep learning, fruit fly algorithm, sentiment analysis, feature selection