Forecasting Consumer Spending from Purchase Intentions Expressed on Social Media

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.

Publication DOI: https://doi.org/10.18653/v1/W17-52
Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
Additional Information: © 2017 The Association for Computational Linguistics. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License.
Last Modified: 03 May 2024 07:25
Date Deposited: 28 Mar 2019 08:37
Full Text Link:
Related URLs: https://www.acl ... hology/W17-5212 (Publisher URL)
PURE Output Type: Conference contribution
Published Date: 2017-09-08
Accepted Date: 2017-01-01
Authors: Pekar, Viktor (ORCID Profile 0000-0002-9664-1675)
Binner, Jane

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