Mining Ethnic Content Online with Additively Regularized Topic Models
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| Format: | Artículo científico |
| Language: | en |
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Instituto Politécnico Nacional
2016
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| _version_ | 1876457454805450752 |
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| author | Murat Apishev |
| author_facet | Murat Apishev |
| contents | Mining Ethnic Content Online with Additively Regularized Topic Models Murat Apishev Sergei Koltcov Olessia Koltsova Sergey Nikolenko Konstantin Vorontsov Computación Topic modeling computational social science additive regularization of topic models Social studies of the Internet have adopted large-scale text mining for unsupervised discovery of topics related to specific subjects. A recently developed approach to topic modeling, additive regularization of topic models (ARTM), provides fast inference and more control over the topics with a wide variety of possible regularizers than developing LDA extensions. We apply ARTM to mining ethnic-related content from Russian-language blogosphere, introduce a new combined regularizer, and compare models derived from ARTM with LDA. We show with human evaluations that ARTM is better for mining topics on specific subjects, finding more relevant topics of higher or comparable quality. 2016 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61547469008 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.3 Vol.20 |
| format | Artículo científico |
| id | redalyc_61547469008 |
| institution | Redalyc |
| language | en |
| publishDate | 2016 |
| publisher | Instituto Politécnico Nacional |
| spellingShingle | Mining Ethnic Content Online with Additively Regularized Topic Models Murat Apishev Computación Topic modeling computational social science additive regularization of topic models Mining Ethnic Content Online with Additively Regularized Topic Models Murat Apishev Sergei Koltcov Olessia Koltsova Sergey Nikolenko Konstantin Vorontsov Computación Topic modeling computational social science additive regularization of topic models Social studies of the Internet have adopted large-scale text mining for unsupervised discovery of topics related to specific subjects. A recently developed approach to topic modeling, additive regularization of topic models (ARTM), provides fast inference and more control over the topics with a wide variety of possible regularizers than developing LDA extensions. We apply ARTM to mining ethnic-related content from Russian-language blogosphere, introduce a new combined regularizer, and compare models derived from ARTM with LDA. We show with human evaluations that ARTM is better for mining topics on specific subjects, finding more relevant topics of higher or comparable quality. 2016 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61547469008 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.3 Vol.20 |
| title | Mining Ethnic Content Online with Additively Regularized Topic Models |
| topic | Computación Topic modeling computational social science additive regularization of topic models |
| url | https://www.redalyc.org/articulo.oa?id=61547469008 |