2018
DOI: 10.1177/0971890718759163
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Analysing Customers Reactions on Social Media Promotional Campaigns

Abstract: Nowadays, it is a general practice by all major firms to launch social media promotional campaigns/ advertisements while launching their new products; however, no study has discussed the customer's reactions on these social media promotional campaigns. In this study, we have analysed customer reactions on social media promotional campaigns launched by three major mobile phone companies selling their phones in India. The analysis has been done on 18,659 tweets by performing part of speech (POS) categorization-b… Show more

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Cited by 9 publications
(4 citation statements)
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“…Specifically, textual responses were sorted, and only those answers with “work” inclusive responses were used in this study. This deeper data was analyzed using Leximancer software, a data-processing tool used widely across numerous research disciplines (Cretchley et al , 2010; Islam et al , 2018; Saxena et al , 2018; Singleton et al , 2018; Smith and Humphreys, 2006), by assessing the qualitative data set and cleaning the open-ended survey data set to exclude nonwords (Larose and Larose, 2015), calculating word frequencies using the sliding n-gram window approach (Michel et al , 2011; Struhl, 2015) where words are counted within sentence-level units across the data document at the rate of once per sentence and tallying all words determined to be important. To process and build themes from these frequency counts, the software accounted for all forms of each word using stemming, created indices of unique terms (Nisbet et al , 2017) and constructed a lexicon of these terms (Struhl, 2015).…”
Section: Methodsmentioning
confidence: 99%
“…Specifically, textual responses were sorted, and only those answers with “work” inclusive responses were used in this study. This deeper data was analyzed using Leximancer software, a data-processing tool used widely across numerous research disciplines (Cretchley et al , 2010; Islam et al , 2018; Saxena et al , 2018; Singleton et al , 2018; Smith and Humphreys, 2006), by assessing the qualitative data set and cleaning the open-ended survey data set to exclude nonwords (Larose and Larose, 2015), calculating word frequencies using the sliding n-gram window approach (Michel et al , 2011; Struhl, 2015) where words are counted within sentence-level units across the data document at the rate of once per sentence and tallying all words determined to be important. To process and build themes from these frequency counts, the software accounted for all forms of each word using stemming, created indices of unique terms (Nisbet et al , 2017) and constructed a lexicon of these terms (Struhl, 2015).…”
Section: Methodsmentioning
confidence: 99%
“…These endeavors were supported (and largely enabled) by huge growth in the volumes of free text available through social media sites and through customer feedback collected by companies and published online. Recent examples include work by Torii, Tilak, Doan, Zisook, and Fan (2016) and Saxena, Chaturvedi, and Rakesh (2018). While many categories have been subject to analysis, the travel and tourism industry has received particular attention (Kirilenko, Stepchenkova, Kim, & Li, 2017; Tkaczynski, Rundle-Thiele, & Cretchley, 2015; Xu, 2018).…”
Section: Text Analysis Todaymentioning
confidence: 99%
“…It helps identify the correct audience for the product, creating relevant content to attract their attention, and then monitoring the impact of the campaign and respond accordingly. Capitalizing on social media can create a viral sensation and assist in the engaging tech-savvy audience [3].…”
Section: Introductionmentioning
confidence: 99%