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- W4312795042 abstract "Social media platforms have presented a way to express the users’ opinions on various topics and connect to friends and share messages, photos, and videos. But there has been an increase in abusive, racial, and hateful messages. As a result, hate tweets have become a significant issue in social media. Detecting hate tweets from Twitter posts with little contextual detail poses several practical problems. Furthermore, the variety of user-generated information and the existence of different hate speech make determining the degree and purpose of the post extremely difficult. A deep belief network with softmax regression is implemented in this work utilizing various embedding techniques for detecting hate speeches in social media. A deep belief network is chosen for resolving the sparse high-dimensional matrix estimation hitch of the text data. Softmax regression is executed to classify the text data in the provided learned feature space, succeeding the feature extraction procedure using hybrid DBN. Experiments are performed on the publicly accessible dataset and evaluate the effectiveness of the deep learning model by considering various metrics." @default.
- W4312795042 created "2023-01-05" @default.
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- W4312795042 date "2022-01-01" @default.
- W4312795042 modified "2023-10-16" @default.
- W4312795042 title "Classification of Hate Tweets Using Hybrid Deep Belief Network Algorithm" @default.
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- W4312795042 doi "https://doi.org/10.1007/978-981-19-5037-7_1" @default.
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