Paper: Automatic Labelling of Topic Models Learned from Twitter by Summarisation

ACL ID P14-2101
Title Automatic Labelling of Topic Models Learned from Twitter by Summarisation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

Latent topics derived by topic models such as Latent Dirichlet Allocation (LDA) are the result of hidden thematic structures which provide further insights into the data. The automatic labelling of such topics derived from social media poses however new challenges since topics may characterise novel events happening in the real world. Existing automatic topic la- belling approaches which depend on exter- nal knowledge sources become less appli- cable here since relevant articles/concepts of the extracted topics may not exist in ex- ternal sources. In this paper we propose to address the problem of automatic la- belling of latent topics learned from Twit- ter as a summarisation problem. We in- troduce a framework which apply sum- marisation algorithms to generate topic la- bels. These algor...