SIG DSA - Data Science and Analytics for Decision Support
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Paper Type
ERF
Paper Number
1306
Description
Researchers operate with limited budgets and inadequate resources. This prohibits big data research and suppresses innovation needed to direct inquiry and construct robust research-based information systems. Such issues are not insuperable, e.g., this project is initialized with limited resources and attempts to build theory, describe architecture, and set the vision for future work. This first “On the Road to …” paper tenders a methodology that examines the use of social media variables as a proxy for human emotion and epistemic activity. A social media corpus is processed and a regression model considers MLB team wins as the dependent variable and a social media tweet corpus, operationalized via NLP, as the independent variable. Results are presented. Future work describes a predictive GIS artifact that will input, process, and visualize a spatial and time-based, NLP processed, social media corpus and is integration with geospatial indexing.
Recommended Citation
Corso, Anthony and Corso, Nathan A., "Will the Home Team Win? On the Road to 1.5 Billion Tweets and Six Thousand Baseball Games Providing Insight!!!" (2022). AMCIS 2022 Proceedings. 15.
https://aisel.aisnet.org/amcis2022/sig_dsa/sig_dsa/15
Will the Home Team Win? On the Road to 1.5 Billion Tweets and Six Thousand Baseball Games Providing Insight!!!
Researchers operate with limited budgets and inadequate resources. This prohibits big data research and suppresses innovation needed to direct inquiry and construct robust research-based information systems. Such issues are not insuperable, e.g., this project is initialized with limited resources and attempts to build theory, describe architecture, and set the vision for future work. This first “On the Road to …” paper tenders a methodology that examines the use of social media variables as a proxy for human emotion and epistemic activity. A social media corpus is processed and a regression model considers MLB team wins as the dependent variable and a social media tweet corpus, operationalized via NLP, as the independent variable. Results are presented. Future work describes a predictive GIS artifact that will input, process, and visualize a spatial and time-based, NLP processed, social media corpus and is integration with geospatial indexing.
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SIG DSA