Name
Abstract | ||
|---|---|---|
Sentiment word identification (SWI) is of high relevance to sentiment analysis technologies and applications. Currently most SWI methods heavily rely on sentiment seed words that have limited sentiment information. Even though there emerge non-seed approaches based on sentiment labels of documents, but in which the context information has not been fully considered. In this paper, based on matrix factorization with co-occurrence neighbor regularization which is derived from context, we propose a novel non-seed model called CONR for SWI. Instead of seed words, CONR exploits two important factors: sentiment matching and sentiment consistency for sentiment word identification. Experimental results on four publicly available datasets show that CONR can outperform the state of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2661829.2662015 | CIKM |
Keywords | Field | DocType |
sentiment lexicon,sentiment word identification,general,matrix factorization,sentiment analysis | Information retrieval,Computer science,Sentiment analysis,Matrix decomposition,Exploit,Regularization (mathematics),Artificial intelligence,Natural language processing | Conference |
Citations | PageRank | References |
9 | 0.76 | 6 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiguang Liang | 1 | 32 | 5.14 |
| Xiaofei Zhou | 2 | 131 | 20.04 |
| Yue Hu | 3 | 108 | 13.42 |
| Li Guo | 4 | 224 | 16.28 |
| Shuo Bai | 5 | 104 | 22.97 |