Forecasting consumer spending from purchase intentions expressed on social media

Viktor Pekar, Jane Binner

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    3 Citations (Scopus)

    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.

    Original languageEnglish
    Title of host publicationEMNLP 2017 - 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA 2017 - Proceedings of the Workshop
    PublisherAssociation for Computational Linguistics, ACL
    Pages92-101
    Number of pages10
    ISBN (Electronic)9781945626951
    Publication statusPublished - 2017
    Event8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA 2017, in conjunction with the Conference on Empirical Methods in Natural Language Processing, EMNLP 2017 - Copenhagen, Denmark
    Duration: 8 Sep 2017 → …

    Publication series

    NameEMNLP 2017 - 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA 2017 - Proceedings of the Workshop

    Conference

    Conference8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA 2017, in conjunction with the Conference on Empirical Methods in Natural Language Processing, EMNLP 2017
    Country/TerritoryDenmark
    CityCopenhagen
    Period8/09/17 → …

    ASJC Scopus subject areas

    • Computational Theory and Mathematics
    • Computer Science Applications
    • Information Systems

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