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Bangla Abusive Language Detection Using Machine Learning on Radio Message Gateway

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dc.contributor.author Ritu, Sumaiya Salim
dc.contributor.author Mondal, Joysurya
dc.contributor.author Mia, Md. Moinu
dc.contributor.author Marouf, Ahmed Al
dc.date.accessioned 2022-04-04T03:52:26Z
dc.date.available 2022-04-04T03:52:26Z
dc.date.issued 2021-08-02
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/7705
dc.description.abstract In the era of modern technology, machine learning and natural language processing has been adopted to be applied in several application areas. Natural language processing consists of diversified techniques such as text classification, text summarization, named entity recognition, sentiment analysis. Text classification is considered to be the area of research where the text gets segmented into different category sentences or paragraphs from a single text genre. This paper presents a mechanism for detecting Bangla abusive language from a real-time radio message gateway. Online radio stations nowadays accept communications and voices of their target audience from web-based applications or social media platforms, such as Facebook or Twitter pages. This paper has created a dataset with more than 45000 Bangla sentences, which are labeled as abusive and non-abusive. Sample online radio message gateway has been introduced and machine learning algorithms such as multinomial naive bias (MNB), logistic regression (LR), and random forest (RF) classifiers are utilized to predict the abusive languages. One of the significant prospects of this work would be applied during live radio programs where listeners try to communicate by sending live messages. Our proposed mechanism can check and map the live messages with the dataset and segregate the positive comments or messages only, by filtering the abusive comments. Among the applied classifiers, it has been found that the random forest classifier has performed better than the other two classifiers by leveraging approximately 76% accuracy. en_US
dc.language.iso en_US en_US
dc.publisher 2021 6th International Conference on Communication and Electronics Systems (ICCES), IEEE en_US
dc.subject Radio frequency en_US
dc.subject Sentiment analysis en_US
dc.subject Machine learning algorithms en_US
dc.subject Social networking (online) en_US
dc.subject Text recognition en_US
dc.subject Text categorization en_US
dc.subject Logic gates en_US
dc.title Bangla Abusive Language Detection Using Machine Learning on Radio Message Gateway en_US
dc.type Article en_US

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