Microblog Topic Identification using Linked Open Data (1804.02158v4)

Abstract: The extensive use of social media for sharing and obtaining information has resulted in the development of topic detection models to facilitate the comprehension of the overwhelming amount of short and distributed posts. Probabilistic topic models, such as Latent Dirichlet Allocation, and matrix factorization based approaches such as Latent Semantic Analysis and Non-negative Matrix Factorization represent topics as sets of terms that are useful for many automated processes. However, the determination of what a topic is about is left as a further task. Alternatively, techniques that produce summaries are human comprehensible, but less suitable for automated processing. This work proposes an approach that utilizes Linked Open Data (LOD) resources to extract semantically represented topics from collections of microposts. The proposed approach utilizes entity linking to identify the elements of topics from microposts. The elements are related through co-occurrence graphs, which are processed to yield topics. The topics are represented using an ontology that is introduced for this purpose. A prototype of the approach is used to identify topics from 11 datasets consisting of more than one million posts collected from Twitter during various events, such as the 2016 US election debates and the death of Carrie Fisher. The characteristics of the approach with more than 5 thousand generated topics are described in detail. The potentials of semantic topics in revealing information, that is not otherwise easily observable, is demonstrated with semantic queries of various complexities. A human evaluation of topics from 36 randomly selected intervals resulted in a precision of 81.0% and F1 score of 93.3%. Furthermore, they are compared with topics generated from the same datasets from an approach that produces human readable topics from microblog post collections.