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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xing Su Songhai Mo Hui Wang Xin Zhang |
| Copyright Year | 2012 |
| Abstract | In this paper, we propose a novel method based on the combination of Named Entity Recognition and Entity Rank algorithm for detecting key entities with significant influence and importance from huge sentiment data collected from Internet. Firstly, we extract entities from the target news websites and forums using a rule-based and CRF combined method. Secondly, we use the Entity Rank algorithm to calculate the hotness of entities extracted from the news and forums data. Finally, we validate the rationality of our algorithm by comparing our hot entities and current affairs. We believe this work will shed new lights on the online public sentiment supervision. |
| Starting Page | 328 |
| Ending Page | 331 |
| File Size | 762836 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781467330930 |
| DOI | 10.1109/MINES.2012.102 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-11-02 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Couplings Named Entity Recognition (NER) EntityRank Hidden Markov models CRF Organizations Tagging PageRank Educational institutions Internet Data mining |
| Content Type | Text |
| Resource Type | Article |
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