在线社交网络中的潜在情感检测：一种面向通信的观点
Latent Sentiment Detection in Online Social Networks: A Communications-oriented View
摘要：
在本文中，我们考虑的问题，潜在的情感检测在线社交网络，如Twitter。我们展示了使用底层社交网络作为伊辛之前执行网络辅助情感检测的好处。我们表明，使用的底层网络的结果在大大降低检测出错率相比，严格的基于特征的检测。在这样做时，我们引入了一个新的通信为导向的框架，描述错误的概率，基于信息理论分析。我们研究了几个程式化的网络拓扑结构，如完整的网络，星形网络和闭链网络的计算误差指数的变化，并显示在确定检测性能的网络结构的重要性。
英文摘要：
In this paper, we consider the problem of latent sentiment detection in Online Social Networks such as Twitter. We demonstrate the benefits of using the underlying social network as an Ising prior to perform network aided sentiment detection. We show that the use of the underlying network results in substantially lower detection error rates compared to strictly features-based detection. In doing so, we introduce a novel communications-oriented framework for characterizing the probability of error, based on information-theoretic analysis. We study the variation of the calculated error exponent for several stylized network topologies such as the complete network, the star network and the closed-chain network, and show the importance of the network structure in determining detection performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
