Toxic detection based on RoBERTa and TF-IDF

Xinmin Liu; Feiyu Zhao

doi:10.1117/12.2641437

10 November 2022 Toxic detection based on RoBERTa and TF-IDF

Xinmin Liu, Feiyu Zhao

Proceedings Volume 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022); 123481C (2022) https://doi.org/10.1117/12.2641437
Event: 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 2022, Zhuhai, China

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.

Citation Download Citation

Xinmin Liu and Feiyu Zhao "Toxic detection based on RoBERTa and TF-IDF", Proc. SPIE 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 123481C (10 November 2022); https://doi.org/10.1117/12.2641437

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available