2020
DOI: 10.1002/cb.1820
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Examining consumer behaviour in the UK Energy sector through the sentimental and thematic analysis of tweets

Abstract: Consumer engagement with brands on social media has been empirically proven. However, little is known about consumers' natural behaviour on social media, as literature on this topic is still in an early stage of its evolution. Accordingly, this study aims to investigate and understand the group interactions of consumer behaviour, with a specific focus on tweets within the UK energy sector. Energy is a significant utility in the United Kingdom, and the sector is evolving more rapidly than ever before, with pres… Show more

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Cited by 29 publications
(18 citation statements)
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References 72 publications
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“…We acknowledge that sentiment analysis alone cannot provide an understanding of people’s experiences and what caused them to have a positive or negative attitude toward a topic or discussion [ 17 ]. Sentiment analysis is a machine learning process involving the application of natural language processing to the identification of expressions that reflect the authors’ opinion-based attitudes toward entities.…”
Section: Discussionmentioning
confidence: 99%
“…We acknowledge that sentiment analysis alone cannot provide an understanding of people’s experiences and what caused them to have a positive or negative attitude toward a topic or discussion [ 17 ]. Sentiment analysis is a machine learning process involving the application of natural language processing to the identification of expressions that reflect the authors’ opinion-based attitudes toward entities.…”
Section: Discussionmentioning
confidence: 99%
“…It is a fact that, as Joyanes-Aguilar (2013) states, data analysis has evolved as large volumes of data grew and, therefore, Big Data analytics excels as the data avalanche grows. Other reference studies such as Cloud Security Alliance (2014) with applications to various sectors and activities (Mogaji & Erkan, 2019;Mogaji, Balakrishnan & Kieu, 2020) account for the importance of linking variables and indicators arising from sophisticated AI-applying services through Big Data approaches to capturing evidence in large volumes of data and understanding the behavior of customers, users or audiences in general (Kunz et al, 2017).…”
Section: Methodsmentioning
confidence: 99%
“…to the identification of utterances that indicate authors' opinion-based attitudes towards items (Li et al 2017). Consistent with the previously accepted methodology for researching energy firms in the UK (Mogaji et al 2021), customer tweets were collected as a direct representation of their interactions with brands and other clients. Python was utilized for Twitter mining and sentiment analysis, notably Twitterscraper and Textblob.…”
Section: Model and Datamentioning
confidence: 99%