Abstract:

The task of aspectlevel opinion mining usually include the extraction of product entities from consumer reviews, the identification of opinion words that are associated with the entities, and the determination of these opinion’s polarities. Aiming at realizing aspectlevel opinion mining for Chinese reviews, the paper proposes the four major steps: preprocessing; preparing the training set to learn the model; defining learning functions for conditional random field model; and applying the model to label new review data. At the same time, our experiments on the real Chinese reviews of five types of products show that the conditional random field based method can achieve 80% in most of performance indicators of extracted different types of review opinion elements. In order to verify the effectiveness of the proposed method, a test of the significance of difference is involved. Experiments report that there is scarcely difference of performance on conditional random field based method for both Chinese reviews and English reviews. Finally, we compare the precision of aspect extraction and the accuracy of sentiment classification based on three different methods, and the result shows that CRFbased method outperforms the other two such as lexicalized hidden markov model and association rule mining.

Key words: conditional random field, aspectlevel opinion mining, opinion elements