Name
Abstract | ||
|---|---|---|
Researchers have been devoted to using context to extract implicit features. However, little concerns have been given to the situation that not all the contexts are meaningful. To solve this problem, we present a new method to evaluate the contribution of the contexts for extracting. We build an improved Co-occurrence matrix that containing the distance between an opinion word and different contexts. And then a LDA topic model is used to get the topic probability of the opinion word. The weight of context can be obtained by using cosine similarity in the improved Co-occurrence matrix and LDA topic model. We design a formula to extract implicit features with the consideration of context and topic. Experiments have showed that our method provides higher accuracy in extracting the implicit features. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/DSAA.2015.7344860 | 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA) |
Keywords | Field | DocType |
implicit features,opinion words,context,matrix,topic model | Cosine similarity,Matrix (mathematics),Computer science,Feature extraction,Context model,Association rule learning,Probability distribution,Artificial intelligence,Topic model,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4673-8272-4 | 0 | 0.34 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jie Chen | 1 | 392 | 65.58 |
| Li Sun | 2 | 1 | 1.36 |
| YingLi Peng | 3 | 1 | 0.69 |
| Yun Huang | 4 | 49 | 5.83 |