Name
Abstract | ||
|---|---|---|
This paper investigates approaches to improve the accuracy of automated sentiment detection in textual knowledge repositories. Many high-throughput sentiment detection algorithms rely on sentiment dictionaries containing terms classified as either positive or negative. To obtain accurate and comprehensive sentiment dictionaries, we merge existing resources into a single dictionary and extend this dictionary by means of semi supervised learning algorithms such as Pointwise Mutual Information - Information Retrieval (PMI-IR) and Latent Semantic Analysis (LSA). The resulting extended dictionary is then evaluated on various datasets from different domains, which were annotated on both the document and sentence level. |
Year | Venue | Keywords |
|---|---|---|
2010 | KDIR 2010: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND INFORMATION RETRIEVAL | Sentiment detection,Natural language processing,Latent semantic analysis,Pointwise mutual information |
Field | DocType | Citations |
Information retrieval,Computer science,Natural language processing,Artificial intelligence | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Johannes Liegl | 1 | 14 | 1.49 |
| Stefan Gindl | 2 | 152 | 9.93 |
| Arno Scharl | 3 | 696 | 67.13 |
| Alexander Hubmann-Haidvogel | 4 | 62 | 6.68 |