Social Media's Impact on the Consumer Mindset: When to Use Which Sentiment Extraction Tool?


Kubler, Raoul V, Colicev, Anatoli ORCID: 0000-0002-3311-8334 and Pauwels, Koen H (2020) Social Media's Impact on the Consumer Mindset: When to Use Which Sentiment Extraction Tool? JOURNAL OF INTERACTIVE MARKETING, 50 (1). pp. 136-155. ISSN 1094-9968, 1520-6653

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Abstract

<jats:p> User-generated content provides many opportunities for managers and researchers, but insights are hindered by a lack of consensus on how to extract brand-relevant valence and volume. Marketing studies use different sentiment extraction tools (SETs) based on social media volume, top-down language dictionaries and bottom-up machine learning approaches. This paper compares the explanatory and forecasting power of these methods over several years for daily customer mindset metrics obtained from survey data. For 48 brands in diverse industries, vector autoregressive models show that volume metrics explain the most for brand awareness and purchase intent, while bottom-up SETs excel at explaining brand impression, satisfaction and recommendation. Systematic differences yield contingent advice: the most nuanced version of bottom-up SETs (SVM with Neutral) performs best for the search goods for all consumer mind-set metrics but Purchase Intent for which Volume metrics work best. For experienced goods, Volume outperforms SVM with neutral. As processing time and costs increase when moving from volume to top-down to bottom-up sentiment extraction tools, these conditional findings can help managers decide when more detailed analytics are worth the investment. </jats:p>

Item Type: Article
Uncontrolled Keywords: Sentiment extraction, Consumer attitudes, Language dictionary, Maching learning, LIWC, Support vector machine, Brand strength, Volume, Valence, User generated content
Divisions: Faculty of Humanities and Social Sciences
Faculty of Humanities and Social Sciences > School of Management
Depositing User: Symplectic Admin
Date Deposited: 07 Oct 2024 13:05
Last Modified: 06 Dec 2024 20:11
DOI: 10.1016/j.intmar.2019.08.001
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3184919
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