Fine-Grained Opinion Mining Using Conditional Random Fields

User generated data and specially product reviews are major sources of information for costumers to make informed purchase decisions and for producers to keep track of consumers opinions. As e-commerce is becoming more and more popular, the number of customer reviews that each product receives grows rapidly. As a result, the problem of automatically mining reviews to extract useful information has recently attracted many researchers. In the last decade, several works have been presented for identifying product aspects from reviews. Product aspects are components or attributes of the product that have been commented on in the review, e.g. 'zoom' and 'battery life' for a digital camera. In this paper, we propose a novel method for mining user opinions, which aims at extracting not only the opinions of users on product aspects, but also a finer level of information indicating the usage type of the aspect. In other words, we try to find out how the reviewer used the aspect. In this work, we focus on the task of identifying product aspects, corresponding opinions, and related usages as a sequence tagging problem. We employ Conditional Random Fields (CRF) to solve the stated problem and propose techniques for defining and filtering features to enhance the accuracy. The accuracy of the proposed method is evaluated using a real life data set from Epinions.com. Experimental evaluation confirms the improved accuracy of our method in identifying aspects, aspect usages, and related opinions. We also evaluate the effectiveness of the optimization techniques through multiple experiments.