Ranking products through online reviews: A novel data-driven method based on interval type-2 fuzzy sets and sentiment analysis
提出一种结合区间二型模糊集和情感分析的数据驱动方法，从在线评论中提取显隐属性、识别情感强度，并用ExpTODIM方法排序产品，以携程旅行产品为例验证有效性。
As an essential information resource, online reviews play an important role in consumers’ decision-making processes. To solve the product ranking problem through online reviews, two important issues are involved: sentiment analysis (SA) for online reviews and product ranking based on multi-criteria decision-making (MCDM) methods. However, merely a few studies have considered the impact of SA accuracy, which can significantly affect the final decision-making process. This paper proposes a novel data-driven method for ranking products through online reviews based on interval type-2 fuzzy sets (IT2FSs) and SA. In this method, after acquiring online reviews, the explicit and implicit attributes are extracted from the website itself and the latent Dirichlet allocation (LDA) model, respectively. Thereafter, a deep learning model is adopted to identify the five sentiment intensities of online reviews, based on which the SA results are represented as IT2FSs by considering the classification effect. After type-reduction for IT2FSs, the ranking order is obtained based on the exponential TODIM (ExpTODIM) method. Furthermore, a case study on ranking travel products from Trip.com Group through online reviews is provided to illustrate the effectiveness and applicability of the proposed method.