Constrained ant brood clustering algorithm with adaptive radius: A case study on aspect based sentiment analysis

摘要

Semi-supervised or constrained clustering refers to clustering data instances in the presence of very limited supervisory information. Although it has been widely investigated in traditional clustering algorithms such as k-means, hierarchical and spectral clustering, little research has addressed the problem of incorporating such knowledge into swarm-intelligence based clustering algorithms. In this study, we present a new Constrained Ant Clustering Algorithm (CACA) with its application to the task of aspect category identification in product reviews, a central clustering task in Aspect-Based Sentiment Analysis (ABSA). We validate our CACA on benchmark datasets, and we show its effectiveness to real-world datasets for ABSA.