Using big data analytics to study brand authenticity sentiments: The case of Starbucks on Twitter

作者：

Highlights：

• A new analytics procedure for studying branding issues in the big data era.

• Tweets are classified under the quality commitment, heritage, uniqueness, and symbolism categories.

• Latent semantic analysis (LSA) extract the common words in each category.

• Results from the support vector machine (SVM) illustrate the effectiveness of the proposed procedure.

• High accuracy for both the brand authenticity dimensions’ prediction and its sentiment polarity.

摘要

•A new analytics procedure for studying branding issues in the big data era.•Tweets are classified under the quality commitment, heritage, uniqueness, and symbolism categories.•Latent semantic analysis (LSA) extract the common words in each category.•Results from the support vector machine (SVM) illustrate the effectiveness of the proposed procedure.•High accuracy for both the brand authenticity dimensions’ prediction and its sentiment polarity.

论文关键词：Brand sentiment analysis,Brand authenticity,Social media,Big data analytics,Support Vector Machine (SVM),Latent semantic analysis (LSA)

论文评审过程：Received 3 April 2017, Revised 8 August 2017, Accepted 28 September 2017, Available online 29 October 2017, Version of Record 14 August 2019.

论文官网地址：https://doi.org/10.1016/j.ijinfomgt.2017.09.007