標題: A Photo Post Recommendation System Based on Topic Model for Improving Facebook Fan Page Engagement
作者: Liao, Chia-Hung
Chen, Li-Xian
Yang, Jhih-Cheng
Yuan, Shyan-Ming
資訊工程學系
Department of Computer Science
關鍵字: Facebook advertising post;social media marketing;text mining;recommendation system;topic model;post engagement
公開日期: 1-七月-2020
摘要: Digital advertising on social media officially surpassed traditional advertising and became the largest marketing media in many countries. However, how to maximize the value of the overall marketing budget is one of the most concerning issues of all enterprises. The content of the Facebook photo post needs to be analyzed effectively so that the social media companies and managers can concentrate on handling their fan pages. This research aimed to use text mining techniques to find the audience accurately. Therefore, we built a topic model recommendation system (TMRS) to analyze Facebook posts by sorting the target posts according to the recommended scores. The TMRS includes six stages, such as data preprocessing, Chinese word segmentation, word refinement, TF-IDF word vector conversion, creating model via Latent Semantic Indexing (LSI), or Latent Dirichlet Allocation (LDA), and calculating the recommendation score. In addition to automatically selecting posts to create advertisements, this model is more effective in using marketing budgets and getting more engagements. Based on the recommendation results, it is verified that the TMRS can increase the engagement rate compared to the traditional engagement rate recommended method (ERRM). Ultimately, advertisers can have the chance to create ads for the post with potentially high engagements under a limited budget.
URI: http://dx.doi.org/10.3390/sym12071105
http://hdl.handle.net/11536/155208
DOI: 10.3390/sym12071105
期刊: SYMMETRY-BASEL
Volume: 12
Issue: 7
起始頁: 0
結束頁: 0
顯示於類別:期刊論文