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

• Published in: Journal of interactive marketing 2020-05, Vol.50 (1), p.136-155
• Main Authors: Kübler, Raoul V., Colicev, Anatoli, Pauwels, Koen H.
• Format: Article
• Language: English
• Subjects: Brand strength; Consumer attitudes; Language dictionary; LIWC; Maching learning; Sentiment extraction; Support vector machine; User generated content; Valence; Volume
• ISSN: 1094-9968; 1520-6653; 1520-0665
• DOI: 10.1016/j.intmar.2019.08.001

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. •Sentiment originating from user generated content from social media platforms such as e.g. Facebook can be used to predict traditional mindset metrics such as e.g. Awareness, Consideration, or Satisfaction.•To extract sentiment from text users have choice among top-down (dictionary based) and bottom-up (machine learning based) approaches.•Choice of sentiment extraction tool for each mindset metric depends on industry and brand factors.•Bottom-up SETs excel at explaining and forecasting the mid-funnel metrics from brand impression to satisfaction.•Systematic differences yield contingent advice: bottom-up SETs work best for strong brands, top-down explains more in purchase intent for weaker brands of experience goods.•Volume-based metrics work best for awareness, impression and satisfaction for stronger brands of experience goods.