Fine-grained opinions are often buried in user reviews. The opinionated aspects may also be associated with different weights by reviewers to represent the aspects' relative importance. As the opinions and weights provide valuable information about users' preferences for products, they can facilitate the generation of personalised recommendations. However, few studies to date have investigated the three inter-connected tasks in a unified framework: aspect identification, aspect-based rating inference and weight estimation. In this paper, we propose a unified framework for performing the three tasks, which involves 1) identifying the product aspects mentioned in a review, 2) inferring the reviewer's ratings for these aspects from the opinions s/he expressed in a review, and 3) estimating the reviewer's weights for these aspects. The relationship among these three tasks is inherently dependent in that the output of one task adjusts the accuracy of another task. We particularly develop an unsupervised model to Collectively estimate Aspect Ratings and Weights (shorted as CARW), which performs all of the three tasks by enhancing each other mutually. We conduct experiments on three real-life datasets to evaluate the CARW model. Experimental results show that the proposed model can achieve better performance than the related methods regarding each task.
Scopus Subject Areas
- Computer Networks and Communications
- Artificial Intelligence
- aspect identification
- aspect-based rating inference
- Review mining
- weight estimation