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dc.contributor.author | Tutubalina E. | |
dc.contributor.author | Nikolenko S. | |
dc.date.accessioned | 2018-09-19T21:50:38Z | |
dc.date.available | 2018-09-19T21:50:38Z | |
dc.date.issued | 2017 | |
dc.identifier.issn | 1865-0929 | |
dc.identifier.uri | https://dspace.kpfu.ru/xmlui/handle/net/144412 | |
dc.description.abstract | © Springer International Publishing AG 2017.We study topic models designed to be used for sentiment analysis, i.e., models that extract certain topics (aspects) from a corpus of documents and mine sentiment-related labels related to individual aspects. For both direct applications in sentiment analysis and other uses, it is desirable to have a good lexicon of sentiment words, preferably related to different aspects in the words. We have previously developed a modification for several popular sentiment-related LDA extensions that trains prior hyperparameters β for specific words. We continue this work and show how this approach leads to new aspect-specific lexicons of sentiment words based on a small set of “seed” sentiment words; the lexicons are useful by themselves and lead to improved sentiment classification. | |
dc.relation.ispartofseries | Communications in Computer and Information Science | |
dc.title | Constructing aspect-based sentiment lexicons with topic modeling | |
dc.type | Conference Paper | |
dc.relation.ispartofseries-volume | 661 | |
dc.collection | Публикации сотрудников КФУ | |
dc.relation.startpage | 208 | |
dc.source.id | SCOPUS18650929-2017-661-SID85014287506 |