MAEC: A Multimodal Aligned Earnings Conference Call Dataset for Financial Risk Prediction
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|Title:||MAEC: A Multimodal Aligned Earnings Conference Call Dataset for Financial Risk Prediction||Authors:||Li, Jiazheng; Yang, Linyi; Smyth, Barry; Dong, Ruihai||Permanent link:||http://hdl.handle.net/10197/12221||Date:||23-Oct-2020||Online since:||2021-05-27T12:04:15Z||Abstract:||In the area of natural language processing, various financial datasets have informed recent research and analysis including financial news, financial reports, social media, and audio data from earnings calls. We introduce a new, large-scale multi-modal, text-audio paired, earnings-call dataset named MAEC, based on S&P 1500 companies. We describe the main features of MAEC, how it was collected and assembled, paying particular attention to the text-audio alignment process used. We present the approach used in this work as providing a suitable framework for processing similar forms of data in the future. The resulting dataset is more than six times larger than those currently available to the research community and we discuss its potential in terms of current and future research challenges and opportunities. All resources of this work are available at https://github.com/Earnings-Call-Dataset/||Funding Details:||Science Foundation Ireland||Funding Details:||Insight Research Centre||Type of material:||Conference Publication||Publisher:||ACM||Copyright (published version):||2020 ACM||Keywords:||Personal sensing; Multimodal aligned datasets; Earnings conference calls; Financial risk prediction||DOI:||10.1145/3340531.3412879||Other versions:||https://www.cikm2020.org/||Language:||en||Status of Item:||Peer reviewed||Is part of:||CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management||Conference Details:||The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, (CIKM '20), 19-23 October 2020||ISBN:||978-1-4503-6859-9||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|
|Appears in Collections:||Insight Research Collection|
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