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Lim, Sunghoon
Unstructured Data Mining and Machine Learning Lab
Research Interests
  • Unstructured Data Mining, Machine Learning, Artificial Intelligence (AI), Natural Language Processing / Text Mining

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Mining Twitter data for causal links between tweets and real-world outcomes

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Title
Mining Twitter data for causal links between tweets and real-world outcomes
Author
Lim, SunghoonTucker, Conrad S.
Issue Date
2019-09
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Citation
EXPERT SYSTEMS WITH APPLICATIONS: X, v.3, pp.100007
Abstract
The authors present an expert and intelligent system that (1) identifies influential term groups having causal relationships with real-world enterprise outcomes from Twitter data and (2) quantifies the appropriate time lags between identified influential term groups and enterprise outcomes. Existing expert and intelligent systems, which are defined as computer systems that imitate the ability of human decision making, could enable computers to identify the spread of Twitter users’ enterprise-related feedback automatically. However, existing expert and intelligent systems have limitations on automatically identifying the causal effects on enterprise outcomes. Identifying the causal effects on enterprise outcomes is important, because Twitter users’ feedback toward enterprise decisions may have real-world implications. The proposed expert and intelligent system can support decision makers’ decisions considering the real-world effects of identified Twitter users’ feedback on enterprise outcomes. In particular, (1) a co-occurrence network analysis model is exploited to discover term candidates for generating influential term groups that are combinations of enterprise-related terms, which potentially influence enterprise outcomes. (2) Time series models and (3) a Granger causality analysis model are then employed to identify influential term groups having causal relationships with enterprise outcomes with the appropriate time lags. Case studies involving a real-world internet video streaming and disc rental provider as well as an airline company are used to test the validity of the proposed expert and intelligent system for both predicting enterprise outcomes in a long period and predicting the effects of specific events on enterprise outcomes in a short period.
URI
https://scholarworks.unist.ac.kr/handle/201301/27223
URL
https://www.sciencedirect.com/science/article/pii/S2590188519300071
DOI
10.1016/j.eswax.2019.100007
ISSN
2590-1885
Appears in Collections:
SME_Journal Papers
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