Name
Title | ||
|---|---|---|
Using multiple sources to construct a sentiment sensitive thesaurus for cross-domain sentiment classification |
Abstract | ||
|---|---|---|
We describe a sentiment classification method that is applicable when we do not have any labeled data for a target domain but have some labeled data for multiple other domains, designated as the source domains. We automatically create a sentiment sensitive thesaurus using both labeled and unlabeled data from multiple source domains to find the association between words that express similar sentiments in different domains. The created thesaurus is then used to expand feature vectors to train a binary classifier. Unlike previous cross-domain sentiment classification methods, our method can efficiently learn from multiple source domains. Our method significantly outperforms numerous baselines and returns results that are better than or comparable to previous cross-domain sentiment classification methods on a benchmark dataset containing Amazon user reviews for different types of products. |
Year | Venue | Keywords |
|---|---|---|
2011 | ACL | amazon user review,similar sentiment,multiple source domain,sentiment sensitive thesaurus,previous cross-domain sentiment classification,source domain,sentiment classification method,different type,unlabeled data,different domain |
Field | DocType | Volume |
Feature vector,Information retrieval,Binary classification,Computer science,Artificial intelligence,Natural language processing,Labeled data | Conference | P11-1 |
Citations | PageRank | References |
23 | 0.83 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| danushka bollegala | 1 | 692 | 66.77 |
| David J. Weir | 2 | 840 | 83.84 |
| John Carroll | 3 | 1971 | 222.19 |