Name
Abstract | ||
|---|---|---|
With the rapid growth of web 2.0, online product reviews generated by users are becoming increasingly useful for customers to make purchase decisions. In this paper, we focus on the problem of classifying user reviews as recommended the product or not. The proposed method first mines the product features and relevant opinions, and then determines the overall sentiment orientation of the review based on the polarity and strength of these opinions. The evaluation results show the effectiveness of our proposed method in product feature mining and review classification. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/APWeb.2010.38 | APWeb |
Keywords | Field | DocType |
online product,user interfaces,web 2.0,sentiment orientation,pattern classification,recommender systems,user review,evaluation result,rapid growth,opinion mining,user reviews,online product reviews,data mining,review classification,product feature mining,purchase decision,product opinion mining,sentiment analysis,product feature,classifying user,opinion extraction,overall sentiment orientation,web 2 0,speech,feature extraction,classification algorithms,machine learning,accuracy,writing,computer science,motion pictures,training data | Recommender system,Data science,Data mining,Information retrieval,Computer science,Sentiment analysis,Feature extraction,Web 2.0,Product reviews,Statistical classification,User interface,Opinion extraction | Conference |
ISBN | Citations | PageRank |
978-1-4244-6600-9 | 2 | 0.41 |
References | Authors | |
6 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sheng Feng | 1 | 2 | 0.41 |
| Ming Zhang | 2 | 1963 | 107.42 |
| Yanxing Zhang | 3 | 2 | 0.41 |
| Zhi-Hong Deng | 4 | 185 | 23.33 |