Learning Semantic Relationships between Entities in Twitter
In this paper, we investigate whether semantic relationships between entities can be learnt from analyzing microblog posts published on Twitter. We identify semantic links between persons, products, events and other entities. We develop a relation discovery framework that allows for the detection of typed relations that moreover may have temporal dynamics. Based on a large Twitter dataset, we evaluate different strategies and show that co-occurrence based strategies allow for high precision and perform particularly well for relations between persons and events achieving precisions of more than 80%. We further analyze the performance in learning relationships that are valid only for a certain time period and reveal that for those types of relationships Twitter is a suitable source as it allows for discovering trending topics with higher accuracy and with lower delay in time than traditional news media.