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Confidence Estimation and Reputation Analysis in Aspect Extraction

Extracting product aspects and their associated sentiments is one of the key tasks in sentiment analysis. Estimating the confidences of extracted aspects is important to ensure the performance. To tackle the issue, this paper proposes a two-step estimation method. Collocations of product features and opinion words are initially extracted through pattern bootstrapping. A criterion synthesizing two measurements, Popularity and Reliability, is novelly exploited to assess both patterns and features. Then the features are further clustered into aspects based on path similarities in the Word Net. Each cluster is assigned a weight based on its Compactness and Texture, and the light ones are filtered out. In addition, this paper also captures global aspect reputations by aggregating sentiment strengths through opinion collocations. Experimental results on a benchmark data set with 5 products demonstrate the effectiveness and reliability of our proposed method.