Product feature extraction based on unsupervised learning
Zhuang Xiong · Computer Engineering and Applications Journal · 2012
The extraction of product feature is one of the important topics in text opinion extraction and sentiment analysis. This paper proposes a method based on unsupervised learning to extract product features. Text patterns are extracted from product review sentences; all the nouns and noun phrases(except product names)in product reviews are expressed as vectors by the feature set constructed by text patterns. All the nouns and noun phrases expressed as vectors are grouped into two sets. The product feature set is identified from the two sets by part-of relation text pat-terns with the help of product names. The experimental results indicate that, the method achieves good result in the corpus of electronic product reviews.