{"id":210,"date":"2018-05-17T20:34:21","date_gmt":"2018-05-18T00:34:21","guid":{"rendered":"https:\/\/sites.bu.edu\/aiem\/?page_id=210"},"modified":"2018-05-17T22:46:46","modified_gmt":"2018-05-18T02:46:46","slug":"dict-based-analysis-and-unsupervised-modeling","status":"publish","type":"page","link":"https:\/\/sites.bu.edu\/aiem\/dict-based-analysis-and-unsupervised-modeling\/","title":{"rendered":"Big Social Data Analytics in Journalism and Mass Communication: Comparing Dictionary-Based Text Analysis and Unsupervised Topic Modeling"},"content":{"rendered":"<h4 style=\"text-align: left;\">Abstract<\/h4>\n<p style=\"color: grey;\">This article presents an empirical study that investigated and compared two \u201cbig data\u201d text analysis methods: dictionary-based analysis, perhaps the most popular automated analysis approach in social science research, and unsupervised topic modeling (i.e., Latent Dirichlet Allocation [LDA] analysis), one of the most widely used algorithms in the field of computer science and engineering. By applying two \u201cbig data\u201d methods to make sense of the same dataset\u201477 million tweets about the 2012 U.S. presidential election\u2014the study provides a starting point for scholars to evaluate the efficacy and validity of different computer-assisted methods for conducting journalism and mass communication research, especially in the area of political communication.<\/p>\n<p style=\"text-decoration: underline;\">Cite the paper:<\/p>\n<div class=\"gs_citr\" tabindex=\"0\">Guo, L., Vargo, C., Pan, Z., Ding, W., &amp; Ishwar, P. (2016). Big Social Data Analytics in Journalism and Mass Communication: Comparing Dictionary-Based Text Analysis and Unsupervised Topic Modeling. <i>Journalism &amp; Mass Communication Quarterly, 93<\/i>(2), 332-359.<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Abstract This article presents an empirical study that investigated and compared two \u201cbig data\u201d text analysis methods: dictionary-based analysis, perhaps the most popular automated analysis approach in social science research, and unsupervised topic modeling (i.e., Latent Dirichlet Allocation [LDA] analysis), one of the most widely used algorithms in the field of computer science and engineering. [&hellip;]<\/p>\n","protected":false},"author":14775,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"https:\/\/sites.bu.edu\/aiem\/wp-json\/wp\/v2\/pages\/210"}],"collection":[{"href":"https:\/\/sites.bu.edu\/aiem\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sites.bu.edu\/aiem\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sites.bu.edu\/aiem\/wp-json\/wp\/v2\/users\/14775"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.bu.edu\/aiem\/wp-json\/wp\/v2\/comments?post=210"}],"version-history":[{"count":6,"href":"https:\/\/sites.bu.edu\/aiem\/wp-json\/wp\/v2\/pages\/210\/revisions"}],"predecessor-version":[{"id":220,"href":"https:\/\/sites.bu.edu\/aiem\/wp-json\/wp\/v2\/pages\/210\/revisions\/220"}],"wp:attachment":[{"href":"https:\/\/sites.bu.edu\/aiem\/wp-json\/wp\/v2\/media?parent=210"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}