Please use this identifier to cite or link to this item:
https://doi.org/10.1109/MIS.2013.45
| Title: | Knowledge-based approaches to concept-level sentiment analysis | Authors: | Cambria, E. Schuller, B. Liu, B. Wang, H. Havasi, C. |
Keywords: | concept-level sentiment analysis data mining intelligent systems knowledge mining online social data opinion mining |
Issue Date: | 2013 | Citation: | Cambria, E., Schuller, B., Liu, B., Wang, H., Havasi, C. (2013). Knowledge-based approaches to concept-level sentiment analysis. IEEE Intelligent Systems 28 (2) : 12-14. ScholarBank@NUS Repository. https://doi.org/10.1109/MIS.2013.45 | Abstract: | The guest editors introduce novel approaches to opinion mining and sentiment analysis that go beyond a mere word-level analysis of text and provide concept-level methods. Such approaches allow a more efficient passage from (unstructured) textual information to (structured) machine-processable data, in potentially any domain. © 2001-2011 IEEE. | Source Title: | IEEE Intelligent Systems | URI: | http://scholarbank.nus.edu.sg/handle/10635/116880 | ISSN: | 15411672 | DOI: | 10.1109/MIS.2013.45 |
| Appears in Collections: | Staff Publications |
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