Name
Abstract | ||
|---|---|---|
This paper describes an approach to utilizing term weights for sentiment analysis tasks and shows how various term weighting schemes improve the performance of sentiment analysis systems. Previously, sentiment analysis was mostly studied under data-driven and lexicon-based frameworks. Such work generally exploits textual features for fact-based analysis tasks or lexical indicators from a sentiment lexicon. We propose to model term weighting into a sentiment analysis system utilizing collection statistics, contextual and topic-related characteristics as well as opinion-related properties. Experiments carried out on various datasets show that our approach effectively improves previous methods. |
Year | Venue | Keywords |
|---|---|---|
2009 | ACL/IJCNLP | sentiment lexicon,collection statistic,utilizing term weight,sentiment analysis task,sentiment analysis system,various term weighting scheme,model term weighting,fact-based analysis task,various datasets,discriminative view,sentiment analysis |
Field | DocType | Volume |
Weighting,Pattern recognition,Computer science,Sentiment analysis,Exploit,Lexicon,Natural language processing,Artificial intelligence,Discriminative model | Conference | P09-1 |
Citations | PageRank | References |
21 | 0.82 | 31 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jungi Kim | 1 | 195 | 13.27 |
| Jin-Ji Li | 2 | 40 | 4.84 |
| Jong-Hyeok Lee | 3 | 740 | 97.88 |