Analyzing Topics of JUUL Discussions on Social Media Using a Semantics-assisted NMF model | |
Hejing Liu1,2,3; Qiudan Li1,2; Riheng Yao1,2,3; Daniel Dajun Zeng1,2,3 | |
2019 | |
会议日期 | 2019-7-1 |
会议地点 | Shenzhen, China |
英文摘要 | JUUL has become a widely used brand of e-cigarettes which takes more than 70% of the market. Social media provides a popular platform for users to discuss the preference and perceptions of JUUL. The discussions are valuable for real-time monitoring of JUUL use. Current research on topic analysis of JUUL discussions mainly relies on human work, which takes much time and effort. This paper adopts a Semantics-assisted NMF topic analysis model to automatically discover topics from JUUL-related short posts on Reddit. By successfully merging the semantic relationships into traditional NMF, this model outperforms in discovering topics with keywords that are important but have a lower word frequency among the posts. Experimental results show the potential of this model in JUUL surveillance and control practice. |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/44314] |
专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心 |
通讯作者 | Qiudan Li |
作者单位 | 1.The State Key Laboratory of Management and Control for Complex Systems , Institute of Automation, Chinese Academy of Sciences Beijing 100190, China 2.Shenzhen Artificial Intelligence and Data Science Institute (Longhua) 3.University of Chinese Academy of Sciences, Beijing, China |
推荐引用方式 GB/T 7714 | Hejing Liu,Qiudan Li,Riheng Yao,et al. Analyzing Topics of JUUL Discussions on Social Media Using a Semantics-assisted NMF model[C]. 见:. Shenzhen, China. 2019-7-1. |
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