TY - JOUR
T1 - Mining and exploring electronic word-of-mouth from Twitter
T2 - case of the Java Jazz Festival
AU - Saragih, Harriman Samuel
N1 - Publisher Copyright:
© 2021, Emerald Publishing Limited.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021
Y1 - 2021
N2 - Purpose: This paper aims to explore textual patterns in ten years of electronic word-of-mouth communications amongst social media (SM) users of the Java Jazz Festival. Design/methodology/approach: This study uses a data-scraping technique to gather user-generated content from Twitter. Word-cloud and word-frequency analysis, along with descriptive coding and pattern matching, are used to categorise the initial findings. Trends and differences in terms of the number of tweets over a ten-year period were examined using analysis of variance and seasonality analysis. Findings: From more than 1.3 million Twitter tweets between 2008 and 2018, this study identified six initial themes. Quantitative analysis revealed that the number of tweets differed significantly in the four quarters of the ten-year period. Research limitations/implications: The results of this study contrast with the claim that digital media communication generally occurs before a festival begins and are least during the festival. Nevertheless, this study supports the notion that SM interaction results in positive consequences, drives conversations amongst users and increases engagement. Practical implications: This study offers five practical implications for music festival organisers and related entities. Originality/value: To the best of the author’s knowledge, this study is the first to provide a systematic and practical data mining and interpretation approach from Twitter within a ten-year period in the Asia Pacific context, through the case of the Java Jazz Festival.
AB - Purpose: This paper aims to explore textual patterns in ten years of electronic word-of-mouth communications amongst social media (SM) users of the Java Jazz Festival. Design/methodology/approach: This study uses a data-scraping technique to gather user-generated content from Twitter. Word-cloud and word-frequency analysis, along with descriptive coding and pattern matching, are used to categorise the initial findings. Trends and differences in terms of the number of tweets over a ten-year period were examined using analysis of variance and seasonality analysis. Findings: From more than 1.3 million Twitter tweets between 2008 and 2018, this study identified six initial themes. Quantitative analysis revealed that the number of tweets differed significantly in the four quarters of the ten-year period. Research limitations/implications: The results of this study contrast with the claim that digital media communication generally occurs before a festival begins and are least during the festival. Nevertheless, this study supports the notion that SM interaction results in positive consequences, drives conversations amongst users and increases engagement. Practical implications: This study offers five practical implications for music festival organisers and related entities. Originality/value: To the best of the author’s knowledge, this study is the first to provide a systematic and practical data mining and interpretation approach from Twitter within a ten-year period in the Asia Pacific context, through the case of the Java Jazz Festival.
KW - Analytics
KW - eWOM
KW - Music festival
KW - Text mining
UR - http://www.scopus.com/inward/record.url?scp=85106323321&partnerID=8YFLogxK
U2 - 10.1108/JHTT-03-2020-0067
DO - 10.1108/JHTT-03-2020-0067
M3 - Article
AN - SCOPUS:85106323321
SN - 1757-9880
VL - 12
SP - 341
EP - 354
JO - Journal of Hospitality and Tourism Technology
JF - Journal of Hospitality and Tourism Technology
IS - 2
ER -