Introduction

Customers dramatically increased their online activity in recent years (Hofacker, Malthouse, & Sultan, 2016). Customer reviews are an effective information source for customers to learn about a product without having to consume it first (Chevalier & Mayzlin, 2006; Hennig-Thurau, Gwinner, Walsh, & Walsh, 2003). Due to technology, the impact of customer reviews on customer behavior is more pronounced than ever. Both academic and business communities acknowledge the importance of online reviews (Breazeale, 2009; Pee, 2016). Customers perceive online reviews to be more trustworthy than traditional marketing (Chevalier & Mayzlin, 2006). Customers use reviews to reduce uncertainty about a product, estimate product quality (Reimer & Benkenstein, 2016) and inform them when buying a product (Lee, Jung, & Park, 2017).

Customer reviews are text information with high dimensionality and multiple latent interpretable topics underlying the texts (Heng, Gao, Jiang, & Chen, 2018). Advanced techniques for linguistic analysis provide the opportunity to extract meaning from reviews. This is valuable for businesses because an understanding of which attributes will enhance compliments or will lead to complaints is important to improve customer satisfaction (Ramanathan & Ramanathan, 2011). Reviews can provide a depiction of customers' perceptions of products. Product quality improvement hints can be captured by analyzing users' online data (Jia, 2019). Scientifically this is important because researchers should not only look at valence and volume of reviews but also examine the content of reviews (Srivastava & Sharma, 2017). However, extant research on the impact of reviews mainly focuses on numerical rations instead of textual reviews (Lee et al., 2017). Cabosky (2016) argues that although current market research studied extensively reviews, significant knowledge gaps still exists. Most research ignores text reviews because of their unstructured nature (Robson, Farshid, Bredican, & Humphrey, 2013). More recently, authors tried to broaden and deepen the understanding of how reviews can influence product performance. However, the applications of text-mining techniques to examine reviews in more depth has been lacking in the literature, partly because of the complexity of text analysis (Heng et al., 2018; Jia, 2019; Lee et al., 2017; Li, Mai, & Wu, 2018).