Name
Abstract | ||
|---|---|---|
This paper considers the problem of document-level multi-way sentiment detection, proposing a hierarchical classifier algorithm that accounts for the inter-class similarity of tagged sentiment-bearing texts. This type of classifier also provides a natural mechanism for reducing the feature space of the problem. Our results show that this approach improves on state-of-the-art predictive performance for movie reviews with three-star and four-star ratings, while simultaneously reducing training times and memory requirements. |
Year | Venue | Keywords |
|---|---|---|
2010 | COLING | state-of-the-art predictive performance,natural mechanism,document-level multi-way sentiment detection,four-star rating,memory requirement,training time,movie review,feature space,inter-class similarity,hierarchical classifier algorithm |
Field | DocType | Volume |
Feature vector,Pattern recognition,Computer science,Artificial intelligence,Hierarchical classifier,Margin classifier,Classifier (linguistics),Machine learning | Conference | C10-1 |
Citations | PageRank | References |
11 | 0.59 | 17 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adrian Bickerstaffe | 1 | 20 | 1.83 |
| Ingrid Zukerman | 2 | 994 | 113.39 |