In this modern era, the dynamic business world has led to the emergence of ’market orientation’, and social CRM (Diffley, McCole, & Carvajal-Trujillo, 2018; Kohli & Jaworski, 1990; Narver & Slater, 1990). The benefits of e-Services are often not fully utilized because of users’ unwillingness to use it (Devaraj & Kohli, 2003; Venkatesh & Davis, 2000). Hence, understanding the user’s motivation in an e-service through Twitter data can help companies better retain users. Though people adopt services quickly, they tend to discontinue the service after limited use. Productivity benefits and maximum Customer Lifetime Value (CLV) are typically obtained in the continued use phase (Kim & Malhotra, 2005; Venkatesh, Morris, Davis, & Davis, 2003). With the emergence of social media, extracting and processing information (Crooks, Croitoru, Stefanidis, & Radzikowski, 2013; Kosala & Blockeel, 2000; Russell, 2011; Sakaki, Okazaki, & Matsuo, 2010), can help in understanding the motivation of users (DeVaro, Kim, Wagman, & Wolff, 2018) towards using an eService. This has motivated us to analyze Twitter data to understand customer motivation levels in eService retention. In this study, 1000 tweets were downloaded from ten different e-Service providers based on the company’s official Twitter handle and analyzed. The results show that using Naïve Bayes on function and content words help in predicting retention intention. Predicting the IS continuance intention of users through tweets analysis can help companies perform better sentiment analysis and provide customized benefits to users. This study can help organizations influence less motivated customers to retain their services through proper marketing strategies.

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Ray, A. & Bala, P. K. (2019). Predicting user motivation towards retention of e-services: An NLP- based approach. International Journal of Business and Administrative Studies, 5(1), 01-08. doi: https://dx.doi.org/10.20469/ijbas.5.10001-1