Sentimental interplay between structured and unstructured user-generated contents : An empirical study on online hotel reviews

User-generated content (UGC), i.e. the feedback from consumers in the electronic market, including structured and unstructured types, has become increasingly important in improving online businesses. However, the ambiguity and heterogeneity, and even the conflict between the two types of UGC, require a better understanding from the perspective of human cognitive psychology. By using online feedback on hotel services, the purpose of this paper is to explore the effects of satisfaction level, opinion dispersion and cultural context background on the interrelationship between structured and unstructured UGC.

Natural language processing techniques – specifically, topic classification and sentiment analysis on the sentence level – are adopted to retrieve consumer sentiment polarity on five attributes relative to itemized ratings. Canonical correlation analyses are conducted to empirically validate the interplay between structured and unstructured UGC among different populations segmented by the mean-variance approach.

The variety of cognitions displayed by individuals affects the general significant interrelationship between structured and unstructured UGC. Extremely dissatisfied consumers or those with heterogeneous opinions tend to have a closer interconnection, and the interaction between valence and dispersion further strengthens or loosens the relationship. The satisfied or neutral consumers tend to show confounding sentiment signals in relation to the two different UGC. Chinese consumers behave differently from non-Chinese consumers, resulting in a relatively looser interplay.

By identifying consistent opinion providers and promoting more valuable UGC, UGC platforms can raise the quality of information generated. Hotels will then be able to enhance their services through the strategic use of UGC by analyzing reviews with dispersed low-itemized rating and by addressing the differences exhibited by non-Chinese customers. This analytical method can also help to create richly structured sentiment information from unstructured UGC.

This paper investigates the variety of cognitive behaviors in the process when UGC are contributed by online reviewers, focussing on the consistency between structured and unstructured UGC. The study helps researchers understanding emotion recognition and affective computing in social media analytics, which is achieved by exploring the variety of UGC information and its relationship to the contributors’ cognitions. The analytical framework adopted also improves the prior techniques.