Character-Level Chinese Toxic Comment Classification Algorithm Based on CNN and Bi-GRU

Zhang, Bao; Wang, Zhongguo

doi:10.1145/3569966.3570000

Cited by 1 publication

(1 citation statement)

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“…The distinct nature of the Chinese language, characterized by its unique grammar, vocabulary, script, and pronunciation, necessitates a detection system specifically tailored for online apps and forum commentary. Addressing the need for a system attuned to Chinese, Zhang and Wang proposed a model that merges a character-level embedded CNN with a Bi-GRU [24]. This innovative approach combines character-and word-level vectors to identify the most important local features within text units.…”

Section: Related Workmentioning

confidence: 99%

Detection System Based on Text Adversarial and Multi-Information Fusion for Inappropriate Comments in Mobile Application Reviews

Yu,

Jia,

Hong

2024

Electronics

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With the rapid development of mobile application technology, the content and forms of comments disseminated on the internet are becoming increasingly complex. Various comments serve as users’ firsthand reference materials for understanding the application. However, some comments contain a significant amount of inappropriate content unrelated to the app itself, such as gambling, loans, pornography, and game account recharging, seriously impacting the user experience. Therefore, this article aims to assist users in filtering out irrelevant and inappropriate messages, enabling them to quickly obtain useful and relevant information. This study focuses on analyzing actual comments on various Chinese apps on the Apple App Store. However, these irrelevant comments exhibit a certain degree of concealment, sparsity, and complexity, which increases the difficulty of detection. Additionally, due to language differences, the existing English research methods exhibit relatively poor adaptability to Chinese textual data. To overcome these challenges, this paper proposes a research method named “blend net”, which combines text adversarial and multi-information fusion detection to enhance the overall performance of the system. The experimental results demonstrate that the method proposed in this paper achieves precision and recall rates both exceeding 98%, representing an improvement of at least 2% compared to existing methods.

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Section: Related Workmentioning

confidence: 99%