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    Bundles: A Framework to Optimise Topic Analysis in Real-Time Chat Discourse.


    Dunne, Jonathan and Malone, David and Penrose, Andrew (2018) Bundles: A Framework to Optimise Topic Analysis in Real-Time Chat Discourse. In: Dunne J., Malone D., Penrose A. (2018) Bundles: A Framework to Optimise Topic Analysis in Real-Time Chat Discourse. In: Rodrigues A., Fonseca B., Preguiça N. (eds) Collaboration and Technology. CRIWG 2018. Lecture Notes in Computer Science, vol 11001. Spr. Lecture Notes in Computer Science (LNCS) (11001). Springer, pp. 60-75. ISBN 9783319995045

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    Abstract

    Collaborative chat tools and large text corpora are ubiquitous in today’s world of real-time communication. As micro teams and start-ups adopt such tools, there is a need to understand the meaning (even at a high level) of chat conversations within collaborative teams. In this study, we propose a technique to segment chat conversations to increase the number of words available (19% on average) for text mining purposes. Using an open source dataset, we answer the question of whether having more words available for text mining can produce more useful information to the end user. Our technique can help microteams and start-ups with limited resources to efficiently model their conversations to afford a higher degree of readability and comprehension.

    Item Type: Book Section
    Additional Information: This is the postprint version of the published article, which is available at Dunne J., Malone D., Penrose A. (2018) Bundles: A Framework to Optimise Topic Analysis in Real-Time Chat Discourse. In: Rodrigues A., Fonseca B., Preguiça N. (eds) Collaboration and Technology. CRIWG 2018. Lecture Notes in Computer Science, vol 11001. Springer, Cham. https://doi.org/10.1007/978-3-319-99504-5_6
    Keywords: chat segmentation; topic modelling; regression;
    Academic Unit: Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Faculty of Science and Engineering > Mathematics and Statistics
    Item ID: 13362
    Identification Number: https://doi.org/10.1007/978-3-319-99504-5_6
    Depositing User: Dr. David Malone
    Date Deposited: 02 Oct 2020 14:41
    Publisher: Springer
    Refereed: Yes
    URI:

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