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Temporal word embeddings for dynamic user profiling in Twitter

Kerin, Breandán orcid logoORCID: 0000-0002-3078-3275, Caputo, Annalina orcid logoORCID: 0000-0002-7144-8545 and Lawless, Séamus orcid logoORCID: 0000-0001-6302-258X (2019) Temporal word embeddings for dynamic user profiling in Twitter. In: 27th AIAI Irish Conference on Artificial Intelligence and Cognitive Science, 5-6 Dec 2019, Galway, Ireland.

The research described in this paper focused on exploring the domain of user profiling, a nascent and contentious technology which has been steadily attracting increased interest from the research community as its potential for providing personalised digital services is realised. An extensive review of related literature revealed that limited research has been conducted into how temporal aspects of users can be captured using user profiling techniques. This, coupled with the notable lack of research into the use of word embedding techniques to capture temporal variances in language, revealed an opportunity to extend the Random Indexing word embedding technique such that the interests of users could be modelled based on their use of language. To achieve this, this work concerned itself with extending an existing implementation of Temporal Random Indexing to model Twitter users across multiple granularities of time based on their use of language. The product of this is a novel technique for temporal user profiling, where a set of vectors is used to describe the evolution of a Twitter user’s interests over time through their use of language. The vectors produced were evaluated against a temporal implementation of another state-of-the-art word embedding technique, the Word2Vec Dynamic Independent Skip-gram model, where it was found that Temporal Random Indexing outperformed Word2Vec in the generation of temporal user profiles.
Item Type:Conference or Workshop Item (Poster)
Event Type:Conference
Uncontrolled Keywords:Natural Language Processing; User Modelling; Word Embeddings; Random Indexing
Subjects:Computer Science > Information retrieval
Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > ADAPT
Published in: Proceedings for the 27th AIAI Irish Conference on Artificial Intelligence and Cognitive Science. . CEUR-WS.
Official URL:http://aics2019.datascienceinstitute.ie/papers/aic...
Copyright Information:© 2019 The Authors
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland under Grant Agreement No. 13/RC/2106, European Union’s Horizon 2020 (EU2020) research and innovation programme under the Marie Skodowska-Curie grant agreement No.: EU2020 713567, The ADAPT SFI Centre for Digital Media Technology is funded by Science Foundation Ireland through the SFI Research Centres Programme and is co-funded under the European Regional Development Fund (ERDF) through Grant # 13/RC/2106
ID Code:24106
Deposited On:08 Jan 2020 12:32 by Annalina Caputo . Last Modified 04 Feb 2020 14:42

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