Social media such as forums, blogs and microblogs has been increasingly used for public information sharing and opinions exchange nowadays. It has changed the way how online community interacts and somehow has led to a new trend of engagement for online retailers especially on microblogging websites such as Twitter. In this study, we investigated the impact of online retailers' engagement with the online brand communities on users' perception of brand image and service. Firstly, we analysed the overall sentiment trends of different brands and the patterns of engagement between companies and customers using the collected tweets posted on a popular social media platform, Twitter. Then, we studied how different types of engagements affect customer sentiments. Our analysis shows that engagement has an effect on sentiments that associate with brand image, perception and customer service of the online retailers. Our findings indicate that the level, length, type and attitude of retailers' engagement with social media users have a significant impact on their sentiments. Based on our results, we derived several important managerial and practical implications.

The purpose of this study is to identify the customers' primary topics of concern regarding online retail brands that are shared among Twitter users. This study collects tweets associated with five leading UK online retailers covering the period from Black Friday to Christmas and New Year's sales. We use a combination of text analytical approaches including topic modelling, sentiment analysis, and network analysis to analyse the tweets. Through the analysis, we identify that delivery, product and customer service are among the most-discussed topics on Twitter. We also highlight the areas that receive the most negative customer sentiments such as delivery and customer service. Interestingly, we also identify emerging topics such as online engagement and in-store experience that are not captured by the existing literature on online retailing. Through a network analysis, we underscore the relationships among those important topics. This study derives insights on how well the leading online retail brands are performing and how their products and services are perceived by their customers. These insights can help businesses understand customers better and enable them to convert the information into meaningful knowledge to improve their business performance. The study offers a novel approach of transforming social media data into useful knowledge about online retailing. The incorporation of three analytical approaches offers insights for researchers to understand the hidden content behind the large collections of unstructured bodies of text, and this information can be used to improve online retailing services and reach out to customers.

Decoding the sentiment dynamics of online retailing customers: Time series analysis of social media. Computers in Human Behavior, 96,[32][33][34][35][36][37][38][39][40][41][42][43][44][45]. https://doi. AbstractThe Twittersphere often offers valuable information about current events. However, despite the enormous quantity of tweets regarding online retailing, we know little about customers' perceptions regarding the products and services offered by online retail brands. Therefore, this study focuses on analysing brand-related tweets associated with five leading UK online retailers during the most important sales period of the year, covering Black Friday, Christmas and the New Year's sales events.We explore trends in customer tweets by utilising a combination of data analytics approaches including time series analysis, sentiment analysis and topic modelling to analyse the trends of tweet volume and sentiment and to understand the reasons underlying changes in sentiment. Through the sentiment and time series analyses, we identify several critical time points that lead to significant deviations in sentiment trends. We then use a topic modelling approach to examine the tweets in the period leading up to and following these critical moments to understand what exactly drives these changes in sentiment. The study provides a deeper understanding of online retailing customer behaviour and derives significant managerial insights that are useful for improving online retailing service provision.Ibrahim, Wang, and Bourne 2017). In addition, an overwhelmingly large volume of data (Netzer, Feldman, Goldenberg, and Fresko 2012) and a lack of practical tools with which to analyse unstructured data (Archak, Ghose, and Ipeirotis 2011) also complicate such analysis. Since Twitter content is produced abundantly every day, changes in customer sentiment and the relationship between those changes and emerging issues on Twitter are difficult to capture. The root cause of such changes may lie in a range of factors, including external events, accidents and disasters, and users' daily lives.Therefore, to understand what exactly drives changes in customer sentiment, this study attempts to exploit the content of these real-time data to understand trends in customer tweets. Thus, the research questions are as follows:• What are the volume and sentiment trends of online retailing brand-related tweets on Twitter?• What are the main reasons behind the changes in the volume and sentiment trends of online retail brand-related tweets?• How can online retailers learn from social media users to improve their online retailing service provision?To answer these questions, we analyse changes in customer sentiment and the underlying customer concerns that evolve through the time series. We examine those tweets or comments posted by customers that are directly related to five leading UK retailers namely Amazon, Argos, Asda, John Lewis and Tesco during the period spanning Black Friday, Christmas, Boxing Day and New Year's Day in the United Kingdom. Using a ...

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