Sentiment Analysis with Global Topics and Local Dependency
| dc.contributor.author | LI, Fangtao | |
| dc.contributor.author | Huang, Minlie | |
| dc.contributor.author | Zhu, Xiaoyan | |
| dc.date.accessioned | 2012-05-22T20:42:25Z | |
| dc.date.available | 2012-05-22T20:42:25Z | |
| dc.date.copyright | 2010 | |
| dc.date.issued | 2010-07 | |
| dc.description | Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-10) | en |
| dc.description.abstract | With the development of Web 2.0, sentiment analysis has now become a popular research problem to tackle. Recently, topic models have been introduced for the simultaneous analysis for topics and the sentiment in a document. These studies, which jointly model topic and sentiment, take the advantage of the relationship between topics and sentiment, and are shown to be superior to traditional sentiment analysis tools. However, most of them make the assumption that, given the parameters, the sentiments of the words in the document are all independent. In our observation, in contrast, sentiments are expressed in a coherent way. The local conjunctive words, such as “and” or “but”, are often indicative of sentiment transitions. In this paper, we propose a major departure from the previous approaches by making two linked contributions. First, we assume that the sentiments are related to the topic in the document, and put forward a joint sentiment and topic model, i.e. Sentiment-LDA. Second, we observe that sentiments are dependent on local context. Thus, we further extend the Sentiment-LDA model to Dependency- Sentiment-LDA model by relaxing the sentiment independent assumption in Sentiment-LDA. The sentiments of words are viewed as a Markov chain in Dependency- Sentiment-LDA. Through experiments, we show that exploiting the sentiment dependency is clearly advantageous, and that the Dependency-Sentiment-LDA is an effective approach for sentiment analysis. | en |
| dc.format | Text | en |
| dc.format.extent | 1 digital file (p. 1371-1376 : ill.) | en |
| dc.identifier.citation | LI, F., Huang, M., & Zhu, X. (2010). Sentiment Analysis with Global Topics and Local Dependency. Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-10). (p. 1371-1376). Association for the Advancement of Artificial Intelligence. | en |
| dc.identifier.isbn | 978-1-57735-463-5 | |
| dc.identifier.uri | http://hdl.handle.net/10625/49059 | |
| dc.language.iso | en | |
| dc.publisher | Association for the Advancement of Artificial Intelligence (AAAI), Palo Alto, California | en |
| dc.subject | SENTIMENT ANALYSIS | en |
| dc.subject | ARTIFICIAL INTELLIGENCE | en |
| dc.subject | MARKOV CHAINS | en |
| dc.title | Sentiment Analysis with Global Topics and Local Dependency | en |
| dc.type | Conference Paper | en |
| idrc.copyright.holder | Association for the Advancement of Artificial Intelligence | |
| idrc.dspace.access | IDRC Only | en |
| idrc.noaccess | Due to copyright restrictions the full text of this research output is not available in the IDRC Digital Library or by request from the IDRC Library. / Compte tenu des restrictions relatives au droit d'auteur, le texte intégral de cet extrant de recherche n'est pas accessible dans la Bibliothèque numérique du CRDI, et il n'est pas possible d'en faire la demande à la Bibliothéque du CRDI. | en |
| idrc.project.componentnumber | 104519006 | |
| idrc.project.number | 104519 | |
| idrc.project.title | International Research Chairs Initiative (IRCI) | en |
| idrc.rims.adhocgroup | IDRC SUPPORTED | en |