An Enhanced Text Mining Approach using Ensemble Algorithm for Detecting Cyber Bullying

An Enhanced Text Mining Approach using Ensemble Algorithm for Detecting Cyber Bullying
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© 2022 by IJETT Journal
Volume-70 Issue-9
Year of Publication : 2022
Authors : Zarapala Sunitha Bai, Sreelatha Malempati
DOI : 10.14445/22315381/IJETT-V70I9P240

How to Cite?

Zarapala Sunitha Bai, Sreelatha Malempati, "An Enhanced Text Mining Approach using Ensemble Algorithm for Detecting Cyber Bullying" International Journal of Engineering Trends and Technology, vol. 70, no. 9, pp. 393-399, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I9P240

Abstract
Text mining (TM) is most widely used to process the various unstructured text documents and process the data present in the various domains. The other name for text mining is text classification. This domain is most popular in many domains, such as movie reviews, product reviews on various E-commerce websites, sentiment analysis, topic modeling, and cyberbullying on social media messages. Cyberbullying is the type of abusing someone with insulting language. Personal abuse, sexual harassment, and other abuse come under cyberbullying. Several existing systems are developed to detect bullying words based on their situation on social networking sites (SNS). SNS becomes a platform for bullying someone. In this paper, An Enhanced text mining approach is developed by using Ensemble Algorithm (ETMA) to solve several problems in traditional algorithms and improve the accuracy, processing time, and quality of the result. ETMA is the algorithm used to analyze the bullying text within the social networking sites (SNS) such as Facebook, Twitter, etc. The ETMA is applied to a synthetic dataset collected from various data sources consisting of 5k messages belonging to bullying and non-bullying. The performance is analyzed by showing Precision, Recall, F1-Score, and Accuracy.

Keywords
Deep Learning, Cyber Bullying, Text Mining, Ensemble Algorithm.

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