Reader Comment Digest through Latent Event Facets and News Specificity
Publication in refereed journal


摘要When a significant event occurs, many news articles from different newsagents often report it. Moreover, these newsagents also provide platforms for their readers to write comments expressing their views or understanding. Through digesting these reader comments, we can gain insights into the reactions, suggestions, personal experiences, or public opinions with respect to the emerging event. However, these reader comments from different sources are often rapidly accumulated resulting in an enormous volume. It becomes difficult to manually analyze these comments. In this paper, we propose a framework that can digest reader comments automatically through latent event facets and news specificity. An event facet refers to the aspect of the event concerned by many readers. Specifically, some of the reader comments, despite coming from different sources, discuss a certain facet of the event. Such facets provide an effective means for organizing news comments in a global manner. On the other hand, some comments discuss the specific topic of the corresponding news article. These specific topics demonstrate the specific focus of readers on the piece of news locally. Such reader comment digest in different granularities facilitates readers deeper understanding of these enormous comments. To achieve the above desirable goal of digesting reader comments, we propose an unsupervised model called EFNS which is capable of capturing the intricate fine-grained associations among events, news, and comments. We also develop a multiplicative-update method to infer the parameters and prove the convergence of our algorithm. Our framework can also visualize reader comments according to the relationship with latent event facets and the degree of news specificity. Experimental results show that our proposed EFNS model can provide an effective way to digest news reader comments and outperform the state-of-the-art method.
著者Bei Shi, Wai Lam
期刊名稱IEEE Transactions on Knowledge and Data Engineering
頁次1581 - 1594

上次更新時間 2021-20-01 於 00:42