Abstract
Consumer spending is a vital macroeconomic indicator. In this paper we present
a novel method for predicting future consumer spending from social media data. In contrast to previous work that largely relied on sentiment analysis, the proposed method models consumer spending from purchase intentions found on social media. Our experiments with time series analysis models and machine-learning regression models reveal utility of this data for making short-term forecasts of consumer spending: for three- and seven-day horizons, prediction variables derived from social media help to improve forecast accuracy by 11% to 18% for all the three models, in comparison to models that used only autoregressive predictors.
a novel method for predicting future consumer spending from social media data. In contrast to previous work that largely relied on sentiment analysis, the proposed method models consumer spending from purchase intentions found on social media. Our experiments with time series analysis models and machine-learning regression models reveal utility of this data for making short-term forecasts of consumer spending: for three- and seven-day horizons, prediction variables derived from social media help to improve forecast accuracy by 11% to 18% for all the three models, in comparison to models that used only autoregressive predictors.
| Original language | English |
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| Title of host publication | Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis |
| Place of Publication | Copenhagen, Denmark |
| Publisher | Association for Computational Linguistics |
| Pages | 92-101 |
| DOIs | |
| Publication status | Published - 8 Sept 2017 |