Automatic Extraction for Product Feature Words from Comments on the Web

Before deciding to buy a product, many people tend to consult others’ opinions on it. Web provides a perfect platform which one can get information to find out the advantages and disadvantages of the product of his interest. How to automatically manage the numerous opinionated documents and then to give suggestions to the potential customers is becoming a research hotspot recently. Constructing a sentiment resource is one of the vital elements of opinion finding and polarity analysis tasks. For a specific domain, the sentiment resource can be regarded as a dictionary, which contains a list of product feature words and several opinion words with sentiment polarity for each feature word. This paper proposes an automatic algorithm to extraction feature words and opinion words for the sentiment resource. We mine the feature words and opinion words from the comments on the Web with both NLP technique and statistical method. Left context entropy is proposed to extract unknown feature words; Adjective rules and background corpus are taken into consideration in the algorithm. Experimental results show the effectiveness of the proposed automatic sentiment resource construction approach. The proposed method that combines NLP and statistical techniques is better than using only NLP-based technique. Although the experiment is built on mobile telephone comments in Chinese, the algorithm is domain independent.