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Visualization of Topic-Sentiment Dynamics in Crowdfunding Projects

Do Carmo, RAF; Kang, SM; Silva, R; (2017) Visualization of Topic-Sentiment Dynamics in Crowdfunding Projects. In: Adams, N and Tucker, A and Weston, D, (eds.) Advances in Intelligent Data Analysis XVI: 16th International Symposium, IDA 2017, London, UK, October 26–28, 2017, Proceedings. (pp. pp. 40-51). Springer: Cham, Switzerland. Green open access

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Abstract

We develop a model that connects the ideas of topic modeling and time series via the construction of topic-sentiment random variables. By doing so, the proposed model provides an easy-to-understand topic-sentiment relationship while also improving the accuracy of regression models on quantitative variables associated with texts. We perform empirical studies on crowdfunding, which has gained mainstream attention due to its enormous penetration in modern society via a variety of online crowdfunding platforms. We study Kickstarter, one of the major players in this market and propose a model and an inference procedure for the amount of money donated to projects and their likelihood of success by capturing and quantifying the importance (sentiment) that possible donors give to the subjects (topics) of the projects. Experiments on a set of 45 K projects show that the addition of the temporal elements adds valuable information to the regression model and allows for a better explanation of the overall temporal behavior of the whole market in Kickstarter.

Type: Proceedings paper
Title: Visualization of Topic-Sentiment Dynamics in Crowdfunding Projects
Event: 16th International Symposium, IDA 2017
Location: London, UK
Dates: 26 October 2017 - 28 October 2017
ISBN-13: 9783319687643
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-319-68765-0_4
Publisher version: http://doi.org/10.1007/978-3-319-68765-0_4
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: topic models, time series, regression
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > UCL School of Management
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10025997
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