Name
Abstract | ||
|---|---|---|
For the task of information retrieval from massive online reviews, people may be faced to some challenges in feature extraction, and then aspects summarization from these features. In this paper, by combining two methods of word vector representing and k-means clustering, an unsupervised method for product aspects summarizing is proposed. The experimental results with real data set verify the validity of the proposed method. Moreover, in comparison with the common LDA like methods, the proposed method shows better performance on both aspect mining and aspect features clustering. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-25159-2_29 | KSEM |
Keywords | Field | DocType |
Aspect mining,Feature extraction,Word vector,Clustering | Data mining,Automatic summarization,Aspect mining,Word representation,Information retrieval,Computer science,Feature extraction,Cluster analysis | Conference |
Volume | ISSN | Citations |
9403 | 0302-9743 | 3 |
PageRank | References | Authors |
0.38 | 11 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kai Ye | 1 | 3 | 0.72 |
| Liangqiang Li | 2 | 4 | 0.73 |
| Mengzhuo Guo | 3 | 3 | 0.38 |
| Yu Qian | 4 | 7 | 3.52 |
| Hua Yuan | 5 | 51 | 8.89 |