Automatic product feature extraction from online product reviews using maximum entropy with lexical and syntactic features

Somprasertsri, Gamgarn; Lalitrojwong, Pattarachai

doi:10.1109/IRI.2008.4583038

Automatic product feature extraction from online product reviews using maximum entropy with lexical and syntactic features

Author(s)

Somprasertsri, Gamgarn

Lalitrojwong, Pattarachai

Date Issued

September 23, 2008

Type

Conference Paper

DOI

10.1109/IRI.2008.4583038

Abstract

The task of product feature extraction is to find product features that customers refer to their topic reviews. It would be useful to characterize the opinions about the products. We propose an approach for product feature extraction by combining lexical and syntactic features with a maximum entropy model. For the underlying principle of maximum entropy, it prefers the uniform distributions if there is no external knowledge. Using a maximum entropy approach, firstly we extract the learning features from the annotated corpus, secondly we train the maximum entropy model, thirdly we use trained model to extract product features, and finally we apply a natural language processing technique in postprocessing step to discover the remaining product features. Our experimental results show that this approach is suitable for automatic product feature extraction. ©2008 IEEE.

Citation

2008 IEEE International Conference on Information Reuse and Integration IEEE Iri 2008, 250-255, 2008