论文标题
社交媒体文本中的利益类型推理
Point-of-Interest Type Inference from Social Media Text
论文作者
论文摘要
物理场所有助于塑造我们如何看待那里的经历。我们第一次研究社交媒体文本与公园，餐厅或其他地方的地点类型之间的关系。为了促进这一点，我们介绍了一个新颖的数据集，$ \ sim $ \ sim $ 200,000英语推文从美国的2,761个不同的利益点发表，并充满了地点类型信息。我们训练分类器，以预测从八个类别到达43.67的宏F1的位置类型，并发现与每种类型相关的语言标记。从推文中预测语义位置信息的能力在推荐系统，个性化服务和文化地理中具有应用。
Physical places help shape how we perceive the experiences we have there. For the first time, we study the relationship between social media text and the type of the place from where it was posted, whether a park, restaurant, or someplace else. To facilitate this, we introduce a novel data set of $\sim$200,000 English tweets published from 2,761 different points-of-interest in the U.S., enriched with place type information. We train classifiers to predict the type of the location a tweet was sent from that reach a macro F1 of 43.67 across eight classes and uncover the linguistic markers associated with each type of place. The ability to predict semantic place information from a tweet has applications in recommendation systems, personalization services and cultural geography.
