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- W4308702581 abstract "In an information age, the presence of toxic content has become a major problem for many online communities, and existing methods are not robust enough to detect it. Therefore, the demand for a more accurate and efficient system for toxic messages detection has reached its peak. In this paper, we introduce machine learning and deep learning models to this task. Following the intuition of acquiring the knowledge of both the word itself and its relationship with other words, a stacking model is constructed as the optimal strategy, combining both term frequency-inverse document frequency method (TF-IDF), a robustly optimized Bidirectional Encoder Representations from Transformers pretraining approach (RoBERTa) as the base-model, and neural network as the meta-model. The experiments show that stacking method and K-fold cross validation are advantageous, and our model achieves a detecting accuracy of 0.9023." @default.
- W4308702581 created "2022-11-14" @default.
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- W4308702581 date "2022-11-11" @default.
- W4308702581 modified "2023-09-30" @default.
- W4308702581 title "Toxic detection based on RoBERTa and TF-IDF" @default.
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- W4308702581 doi "https://doi.org/10.1117/12.2641437" @default.
- W4308702581 hasPublicationYear "2022" @default.
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