@InProceedings{pekar-binner:2017:WASSA2017,
  author    = {Pekar, Viktor  and  Binner, Jane},
  title     = {Forecasting Consumer Spending from Purchase Intentions Expressed on Social Media},
  booktitle = {Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {92--101},
  abstract  = {Consumer spending is an important macroeconomic indicator that is used by
	policy-makers to judge the health of an economy. 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.},
  url       = {http://www.aclweb.org/anthology/W17-5212}
}