## 1 Introduction  
In big data era, the research paradigms of marketing service have been greatly changed by the enormous marketing data accumulated from the internet, such as on demographics, user behavior (we will henceforth use the word \"user\", \"customer\", and \"consumer\" interchangeably), and social relationships. These fine-grained marketing data are informative, thus providing marketers with extra opportunities to evaluate users' preferences [1], predicting the next product users will buy [2; 3], delivering targeted advertisements [4; 5], uncovering consumer perceptions of brand [6], and acquiring competitive intelligence [7]. In particular, psychological aspects of user behavior, critical for understanding rather than merely predicting consumer preference, ultimately contribute to intelligent decision-making in marketing. Unfortunately, investigations of how big data helps inform has, thus far, only received scant attention [8]. Studies have demonstrated that psychological variables, such as value and personality, are the main theories and tools for user psychographic segmentation, which is an important determinant of user purchase behaviors and preferences [9; 10]. In particular, personality traits, one of the psychographic segmentation components, has been the main consumer psychological and marketing characteristic used in e-commerce. Unfortunately, the predictive and explanatory power of personality traits for online user behavior remains controversial [11]. This controversy motivates the investigation of other types of psychographic segmentation, such as value, in understanding user preferences, which have not been investigated. Furthermore, it is unclear whether the predictive and explanatory power varies between product categories and between different segment-wise preference prediction methods. The main reason is that collecting psychographic segmentation data from e-commerce users is difficult on a large scale, typically requiring consumers to complete lengthy questionnaires. Psychological variables, such as personality and value, are deeply embedded in the language that people use today [12]. With massive user data in the form of natural language, natural language data provides a clearer picture of people's cognitive and behavioral processes than data collected from traditional and widely used self-report surveys [13; 14; 15]. Practically, natural language processing (NLP) techniques can be applied to identifying psychographic variables, such as e-commerce users' online word use, to understand and predict users' purchase behaviors and preferences on a large scale. E-commerce websites, in particular, have accumulated a large amount of user-generated content (UGC), which provides the basis for observing users' psychographics and predicting user preferences directly. With the rapid development of techniques such as big data, artificial intelligence, and computational linguistics, UGC provides a reliable path for automatically identifying consumer psychographics, including personality and values, based on unstructured data. Inspired by recent advances in big data-driven psycholinguistic research, which indicate the behavioral evidence of online word use related to psychographics [16; 17], we base our research on real-world Amazon consumer review and rating data. We propose psychographics-related word use evidence, extract consumers' psychographic characteristics using sentiment analysis methods, and introduce a deep neural network (DNN) to predict and explain user preferences in e-commerce. We found that, overall, psychographic variables significantly improved the explanatory power of e-consumer preferences across most product categories we studied, whereas the improvement in predictive power was not significant. Specifically, the Schwartz Value Survey (SVS) tended to outperform Big Five Factor (BFF) segmentation in predicting and explaining user preferences, with the exception of a few product categories.