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        <title>Forecasting Consumer Spending from Purchase Intentions Expressed on Social Media</title>
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        <namePart type="given">Viktor</namePart>
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            <title>Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis</title>
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        <name type="personal">
            <namePart type="given">Alexandra</namePart>
            <namePart type="family">Balahur</namePart>
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        <name type="personal">
            <namePart type="given">Saif</namePart>
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            <namePart type="given">Erik</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/W17-5212</identifier>
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%0 Conference Proceedings
%T Forecasting Consumer Spending from Purchase Intentions Expressed on Social Media
%A Pekar, Viktor
%A Binner, Jane
%Y Balahur, Alexandra
%Y Mohammad, Saif M.
%Y van der Goot, Erik
%S Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F pekar-binner-2017-forecasting
%X 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.
%R 10.18653/v1/W17-5212
%U https://aclanthology.org/W17-5212
%U https://doi.org/10.18653/v1/W17-5212
%P 92-101

Markdown (Informal)

[Forecasting Consumer Spending from Purchase Intentions Expressed on Social Media](https://aclanthology.org/W17-5212) (Pekar & Binner, WASSA 2017)

• Forecasting Consumer Spending from Purchase Intentions Expressed on Social Media (Pekar & Binner, WASSA 2017)

ACL

• Viktor Pekar and Jane Binner. 2017. Forecasting Consumer Spending from Purchase Intentions Expressed on Social Media. In Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 92–101, Copenhagen, Denmark. Association for Computational Linguistics.