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An updated framework based on LDA is provided in this paper to extract features from online user reviews which are in Chinese. This model is an extension of the LDA by introducing the concepts of multi-gram and part of speech into it and it is named PM-LDA. Through this model, features are generated as topics and topic labels can be generated as the sentence that has the max topic probability. Topics in PM-LDA are divided into two different types. The one is some general features such as product brand, color or producing area, and the other is those latent characteristics which customers may be more interested in. Part of speech is used to get the feature object and feature opinion separately, to make it more accurate. Several experiments are carried out to help to evaluate this model and it is indicated that this method has improved performance both in accuracy and the quality of the topic model itself.