In the Web2.0 era, user-generated content (UGC) provides a valuable source of data to aid in understanding consumers and driving intelligent business. Text mining techniques, such as semantic analysis and sentiment analysis, help to extract meaningful information embedded in UGC. However, research on text mining of UGC for e-commerce business applications involves interdisciplinary knowledge, and few studies have systematically summarized the research framework and application directions of related research in this field. First, based on e-commerce practice, in this study, we derive a general framework to summarize the mainstream research in this field. Second, widely used text mining techniques are introduced, including semantic and sentiment analysis. Furthermore, we analyze the development status of semantic analysis in terms of text representation and semantic understanding. Then, the definition, development, and technical classification of sentiment analysis techniques are introduced. Third, we discuss mainstream directions of text mining for business applications, ranging from high-quality UGC detection and consumer profiling, to product enhancement and marketing. Finally, research gaps with respect to these efforts are emphasized, and suggestions are provided for future work. We also provide prospective directions for future research.

Virtual reality (VR) tourism e-commerce platforms (VRTEPs) can provide trial experience for tourists, which is easy to trigger irrational impulse buying. Based on cognitive emotion theory and S-O-R model, this study innovatively explores the impact of the multi cue-interaction stimulation (the interactivity of VR display and the empathy of text display) of VRTEPs on consumers’ impulse buying intention through the mediating effect of new cognitive and emotional responses, namely, interactive pleasure (perceived usefulness and immersion). The results reveal that: (1) The interactivity of VR display stimulates interactive pleasure, then positively affects impulse buying intention. (2) The empathy of text display only stimulates perceived usefulness to positively affect impulse buying intention. This study also finds that the interactive comparison has a negative moderating effect on the relationship between immersion and impulse buying intention. These findings contribute to developing the new theory for effectively marketing using highly interactive new technology on VRTEPs.

3D printing is the important part of the emerging industry, and the accurate prediction of technology hot spots (THS) in the 3D printing industry is crucial for the strategic technology planning. The patents of the THS are always in the minority and have outlier characteristics, so the existing single and rigid models cannot accurately and robustly predict the THS. In order to make up for the shortcomings of the existing research, this study proposes a model for robust composite attraction indicator (MRCAI), which avoids the impact of outlier patents on prediction accuracy depending on not only extracting the patent attraction indicators (AIs) but also constructing the robust composite attraction indicator (CAI) according to the rough consensus of predicted results of CAIs with high generalization. Specifically, firstly, this study selects the patent AIs from the four dimensions of the attraction: technology group attraction, state attraction, enterprise attraction and inventor attraction. Secondly, in order to completely describe the attraction features of patent, AIs are directly and indirectly integrated into CAIs. Thirdly, we reduce the influence of outlier patents on prediction accuracy from two aspects: on the one hand, we initially select the CAIs with good generalization performance based on the prediction error fluctuation range. On the other hand, we build the robust CAIs by calculating the consensus of CAIs with high generalization performance based on the rough set. Fourthly, the 3D printing industry technology attention matrix is constructed to map the effective technology strategic planning based on predicted patent backward citation count by MRCAI in the short, medium and long term. Finally, the experimental results on 3D printing patent data show that MRCAI can effectively improve the efficiency in dealing with samples with outlier patents and has strong flexibility and robustness in predicting the THS in 3D printing industry.

Peer-to-peer (P2P) online experience offers low-cost immersive cultural experiences, but enterprises fear that it will negatively affect local tourism marketing. This study proposes a sequential mediation model to explore how P2P online experience stimulates travel intentions and attraction recommendations. We find that P2P online experience increases cultural presence, which in turn leads to higher situational empathy. This increased empathy then contributes to predicted tourism experience, which ultimately shapes behavioral intentions toward travel and attraction recommendations. Furthermore, the study finds that the impact of P2P online experience on behavioral intentions is stronger for consumers with lower cultural cognitive levels of tourist attractions. Our findings demonstrate the importance of P2P online experience on cultural presence and situational empathy in shaping travel behavior.