Name
Abstract | ||
|---|---|---|
Sentiment is mainly analyzed at a document, sentence or aspect level. Document or sentence levels could be too coarse since polar opinions can co-occur even within the same sentence. In aspect level sentiment analysis often opinion-bearing terms can convey polar sentiment in different contexts. Consider the following laptop review: "the big plus was a large screen but having a large battery made me change my mind," where polar opinions co-occur in the same sentence, and the opinion term that describes the opinion targets ("large") encodes polar sentiments: a positive for screen, and a negative for battery. To parse these differences, our approach is to identify opinions with respect to the specific opinion targets, while taking the context into account. Moreover, considering that there is a problem of obtaining an annotated training set in each context, our approach uses unlabeled data. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-12024-9_3 | Communications in Computer and Information Science |
Keywords | Field | DocType |
Fine-grained sentiment analysis,Opinion mining,Lexicon | Training set,Laptop,Sentiment analysis,Computer science,Lexicon,Natural language processing,Artificial intelligence,Parsing,Sentence | Conference |
Volume | ISSN | Citations |
475 | 1865-0929 | 1 |
PageRank | References | Authors |
0.36 | 15 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nir Ofek | 1 | 80 | 7.69 |
| Lior Rokach | 2 | 2127 | 142.59 |