Generating a concept hierarchy for sentiment analysis

In this paper, we propose an unsupervised machine learning method to automatically construct a product hierarchical concept model based on the online reviews of this product. Our method starts by representing each candidate noun using a feature context vector, which is simply a vector of all its co-occurring neighbors excluding itself. We then applied bisection clustering to hierarchically cluster the context vectors to obtain a cluster hierarchy. Lastly, we proposed and evaluated two methods to label each intermediate clustering node with the most representative member context feature vector. Experiments conducted on 3 sets of on-line reviews (in both Chinese and English) benchmarked qualitatively and quantitatively against a well known existing approach demonstrated the effectiveness and robustness of our approach.