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Analysis of Weibo Comments Based on SVM and LDA Models

机译:基于SVM和LDA模型的微博评论分析

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Nowadays, Sina Weibo is an important way for people to voice their opinions in China. With the development of social media computing, we can find out public opinions of an event by analyzing Weibo comments using computational methods. In this paper, we focus on a hospital event which happened in China. First, we collect comments about the event from Sina Weibo blogs of 4 different kinds of bloggers including traditional media, medical media, Internet celebrity and actor self-media. Then we estimate the sentiment of the comments by making use of Support Vector Machine (SVM) model. Finally, we extract topics from the comments by using the Latent Dirichlet Allocation (LDA) topic model for the purpose of explaining the reason of different sentiment for each blogger.
机译:如今,新浪微博是人们在中国发表意见的重要途径。随着社交媒体计算的发展,我们可以通过使用计算方法分析微博评论来了解事件的公众意见。在本文中,我们专注于在中国发生的医院活动。首先,我们从新浪微博博客中收集4种不同类型博客的活动,包括传统媒体,医疗媒体,互联网名人和演员自我媒体。然后我们通过利用支持向量机(SVM)模型来估计评论的情绪。最后,我们通过使用潜在的Dirichlet分配(LDA)主题模型来提取评论的主题,以解释每个博主的不同情绪的原因。

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