The importance of text mining is increasing in services management as the access to big data is increasing across digital platforms enabling such services. This study adopts a systematic literature review on the application of text mining in services management. First, we analyzed the literature on which has used text mining methods like Sentiment Analysis, Topic Modeling, and Natural language Processing (NLP) in reputed business management journals. Further, we applied visualization tools for text mining and the topic association to understand the dominant themes and relationships. The analysis highlighted that social media analysis, market analysis, competitive intelligence are the most dominant themes while other themes like risk management and fake content detection are also explored. Further, based on the analysis, future research agenda in the field of text mining in services management has been indicated.
An immense amount of digitized texts is available on the online platform in newspaper content, social media posts, customers’ reviews on products and their experiences, scientific articles, and press releases. From some time back, scholars from management have started utilizing the power of text mining in various fields for theory building (Kar and Dwivedi, 2020). In fact, inclination towards digital transformation of industries has increased the quality and volume of unstructured data. This ample amount of unstructured data is now available for the researchers for analysis using big data analytics methodology, of which text mining is an integral part. Methods for theory building is gaining traction which uses text analytics on these large unstructured data (Kar and Dwivedi, 2020).
Further, the Internet's immense growth allows users to share and search for ideas, opinions, and recommendations. Social media platforms like Twitter and Facebook play an important role in direct and indirect communication among users (Chandler et al., 2018). Besides, this massive amount of data available on social media platforms opens a new opportunity for the researchers and market professionals to analyze the status of the product in the market and help to make strategic plans for the growth of products and services as well (Shirdastian et al., 2017). The number of Internet users, as well as social media users, is growing day by day. Therefore, users create vast amounts of data in texts, videos, images, and audios on social media platforms. In addition, data on social media is freely available to users. Therefore, users can extract huge amounts of data from social media platforms in a concise duration. Here, the text mining technique is applied to social media data for many purposes like marketing (AlAlwan et al., 2017), product planning (Jeong et al., 2017), and digital marketing (Aswani et al., 2018). Online platforms like Yelp and TripAdvisor provide a platform for the customers or consumers to contribute fair feedback and explicit opinions to service providers. Therefore, online reviews are a reliable and free source of consumers' or customer's feedback in this digital environment (Dellarocas, 2003).