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Making sense of consumers’ tweets: sentiment outcomes for fast fashion retailers through big data analytics

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)915-927
Number of pages13
JournalInternational Journal of Retail and Distribution Management
Volume47
Issue number9
Early online date7 Nov 2018
DOIs
DateAccepted/In press - 27 Aug 2018
DateE-pub ahead of print - 7 Nov 2018
DatePublished (current) - 9 Sep 2019

Abstract

Purpose- Consumers online interactions, posts, rating and ranking, reviews of products/attractions/restaurants and so on lead to a massive amount of data that marketers might access to improve the decision-making process, by impacting the competitive and marketing intelligence. The aim of this research is to help to develop understanding of consumers online generated contents in terms of positive or negative comments to increase marketing intelligence.

Design/Methodology/Approach- The research focuses on the collection of 9,652 tweets referring to three fast fashion retailers of different sizes operating in the UK market, which have been shared among consumers and between consumer and firm, and subsequently evaluated through a sentiment analysis based on machine learning.

Findings- Findings provide the comparison and contrast of consumers’ response towards the different retailers, while providing useful guidelines to systematically making sense of consumers’ tweets and enhancing marketing intelligence.

Practical Implications- Our research provides an effective and systemic approach to (i) accessing the rich data set on consumers’ experiences based the massive number of contents that consumers generate and share online, and (ii) investigating this massive amount of data to achieve insights able to impact on retailers’ marketing intelligence.

Originality/Value- To best of our knowledge, while other authors tried to identify the effect of positive or negative online comments/posts/reviews, the present study is the first one to show how to systematically detect the positive or negative sentiments of shared tweets for improving the marketing intelligence of fast fashion retailers.

    Structured keywords

  • MGMT Marketing and Consumption

    Research areas

  • Online consumer behaviour, Fast fashion, Big Data analytics, Consumer-generated contents, E-word of mouth communication, User-generated contents (UGC)

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  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Emerald at https://www.emeraldinsight.com/doi/full/10.1108/IJRDM-07-2018-0127 . Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 338 KB, PDF document

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