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Their experiments show that topic-based models generated with Latent Dirichlet Allocation (LDA) yield, most of the times, better categorizations when compared to TF-IDF based features, particularly when these models are enriched with natural language features and specific Twitter slang.<\/p>","DOI":"10.4018\/ijswis.2013070101","type":"journal-article","created":{"date-parts":[[2013,12,30]],"date-time":"2013-12-30T15:41:31Z","timestamp":1388418091000},"page":"1-13","source":"Crossref","is-referenced-by-count":6,"title":["Semantic Characterization of Tweets Using Topic Models"],"prefix":"10.4018","volume":"9","author":[{"given":"Andr\u00e9s","family":"Garc\u00eda-Silva","sequence":"first","affiliation":[{"name":"Ontology Engineering Group, Universidad Polit\u00e9cnica de Madrid, Madrid, Spain"}]},{"given":"V\u00edctor","family":"Rodr\u00edguez-Doncel","sequence":"additional","affiliation":[{"name":"Ontology Engineering Group, Universidad Polit\u00e9cnica de Madrid, Madrid, Spain"}]},{"given":"Oscar","family":"Corch","sequence":"additional","affiliation":[{"name":"Ontology Engineering Group, Universidad Polit\u00e9cnica de Madrid, Madrid, Spain"}]}],"member":"2432","reference":[{"key":"ijswis.2013070101-0","doi-asserted-by":"crossref","unstructured":"Asur, S., & Huberman, B. 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