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Rao, Yanghui
IEEE intelligent systems, 2016-Jan.-Feb., 2016-1-00, 20160101, Letnik: 31, Številka: 1Journal Article
Social emotion classification is important for numerous applications, such as public opinion measurement, corporate reputation estimation, and customer preference analysis. However, topics that evoke a certain emotion in the general public are often context-sensitive, making it difficult to train a universal classifier for all collections. A multilabeled sentiment topic model, namely, the contextual sentiment topic model (CSTM), can be used for adaptive social emotion classification. The CSTM distinguishes context-independent topics from both a background theme, which characterizes nondiscriminative information, and a contextual theme, which characterizes context-dependent information across different collections. Experimental results demonstrated the effectiveness of this model for the adaptive classification of social emotions.
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Dostop do baze podatkov JCR je dovoljen samo uporabnikom iz Slovenije. Vaš trenutni IP-naslov ni na seznamu dovoljenih za dostop, zato je potrebna avtentikacija z ustreznim računom AAI.
Leto | Faktor vpliva | Izdaja | Kategorija | Razvrstitev | ||||
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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
Baze podatkov, v katerih je revija indeksirana
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Povezave do osebnih bibliografij avtorjev | Povezave do podatkov o raziskovalcih v sistemu SICRIS |
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Vir: Osebne bibliografije
in: SICRIS
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