A fuzzy aspect‐based approach for recommending hospitals

Abstract

The process to assess a hospital performance usually needs the interaction of a lot of experts and patients and is very costly and time-consuming. Nevertheless, the availability of patient opinions on the Internet offers a great opportunity to develop systems that evaluate hospitals based on user feedback. The content of these opinions is very challenging, including information about the hospital services but also stories about their own patients, their families, and personal feelings or beliefs before or after leaving a hospital. Therefore, the task of recommending hospitals according to the quality of their services becomes really complicated. This study describes an application for ranking hospitals based on the user preferences about the different offered services as well as the opinions about them. First, it semiautomatically classifies all predefined hospital aspects, calculates the sentiment orientation, and represents their associated polarity by intuitionistic fuzzy sets. Second, by means of the user preferences towards the different aspects, an aggregation operator, and a multicriteria decision-making algorithm, all hospitals are ranked. To assess this methodology, a large set of reviews about hospitals have been collected.

Multi-strategy KOA Algorithm for Optimizing Gated Recurrent Cell Networks in Automatic Writing Scoring Method Design

“…Currently, the research on automatic writing scoring methods is mainly carried out from two aspects: automatic writing scoring feature extraction and automatic writing scoring model construction [7]. Writing automatic scoring feature extraction is mainly studied from the aspects of word feature extraction [8], semantic feature extraction [9], and topic feature representation [10]. Literature [11] proposes the matching rate of n-order word elements as the scoring rule, and at the same time introduces the length penalty ratio to solve the problem of high scores for short sentences; literature [12] analyzes the writing scoring features from the perspectives of literal overlap, keywords, semantics, etc., and constructs the writing automatic scoring model.…”