Assessing the helpfulness of hotel reviews for information overload: a multi-view spatial feature approach

Consumer perceptions of helpfulness remain an open question due to the lack of semantic and spatial features of review content. This paper aims to explore three aspects of the contents of a review: time, rating, and location, to assess the helpfulness of hotel reviews. A multi-view graph convolutional network (MVGCN) and attention mechanisms that capture multimodal semantic information are designed. The experimental results on Yelp and TripAdvisor are evaluated. The findings indicate that this facilitates the filtering of helpful information and avoids information overload when reading to customers. The results show that the proposed model outperforms the baseline and illustrates the interpretability of the models in each view. Our work is essential for professionals of both hotel and travel platforms that can utilize our findings to optimize their sales systems. Also, the results can help visitors or users acquire beneficial information and avoid information overload. This study is one of the few articles that can promote a model interpretable for information overload, which aims to guide research on evaluating the helpfulness of reviews in the hotel sector. This study contributes also to the methodology by developing extracting features of multimodal data, giving a multi-view feature with several novel assessments, and a novel framework involving deep learning.