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

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Making sense of consumers’ tweets : sentiment outcomes for fast fashion retailers through big data analytics. / Pantano, Eleonora; Giglio, Simona; Dennis, Charles.

In: International Journal of Retail and Distribution Management, 07.11.2018.

Research output: Contribution to journalArticle

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Pantano, Eleonora ; Giglio, Simona ; Dennis, Charles. / Making sense of consumers’ tweets : sentiment outcomes for fast fashion retailers through big data analytics. In: International Journal of Retail and Distribution Management. 2018.

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@article{101937bbdecf48e395fcfd0539295651,
title = "Making sense of consumers’ tweets: sentiment outcomes for fast fashion retailers through big data analytics",
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.",
keywords = "Online consumer behaviour, Fast fashion, Big Data analytics, Consumer-generated contents, E-word of mouth communication, User-generated contents (UGC)",
author = "Eleonora Pantano and Simona Giglio and Charles Dennis",
year = "2018",
month = "11",
day = "7",
doi = "10.1108/IJRDM-07-2018-0127",
language = "English",
journal = "International Journal of Retail and Distribution Management",
issn = "0959-0552",

}

RIS - suitable for import to EndNote

TY - JOUR

T1 - Making sense of consumers’ tweets

T2 - sentiment outcomes for fast fashion retailers through big data analytics

AU - Pantano, Eleonora

AU - Giglio, Simona

AU - Dennis, Charles

PY - 2018/11/7

Y1 - 2018/11/7

N2 - 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.

AB - 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.

KW - Online consumer behaviour

KW - Fast fashion

KW - Big Data analytics

KW - Consumer-generated contents

KW - E-word of mouth communication

KW - User-generated contents (UGC)

U2 - 10.1108/IJRDM-07-2018-0127

DO - 10.1108/IJRDM-07-2018-0127

M3 - Article

JO - International Journal of Retail and Distribution Management

JF - International Journal of Retail and Distribution Management

SN - 0959-0552

ER -