A BTM-Based Adaptive Objectionable Short Text Filtering Framework. (22nd January 2022)

• Record Type: Journal Article
• Authors: Cui, Dong; Wen, Qiaoyan; Zhang, Hua; Li, Wenmin; Shi, Yijie; Zhou, Yingyu; Zhang, Lei
• Other Names: Prieto Javier Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2022(2022)
• Publication Date: 2022-01-22
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2022/6668344
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : Many methods are available for objectionable text filtering, such as URL-based filtering, keyword-based filtering, and intelligence-based analysis filtering approaches. URL-based filtering cannot filter the contents of objectionable short text. Keyword-based filtering faces the overblocking issue. Intelligence-based analysis filtering is inefficient and ineffective when filtering objectionable short text. In this paper, a biterm topic modelling- (BTM-) based adaptive objectionable short text filtering framework is proposed. We propose a feature extraction algorithm for objectionable short text and establish a sensitive word feature dataset using the descriptions of applications on the Internet. Then, we construct a judgment standard to automatically select the K value of the BTM topic model that can induce self-adaptation. The feature dataset constructed in this paper can effectively reflect the characteristics of objectionable short text. The proposed filtering framework can effectively identify objectionable short text and has a higher filtering rate than other approaches.