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In this scenario the interestingness is derived from the sentiment, temporal density, and context coherence that comments about features for different targets (e.g., persons, institutions, product attributes, topics, etc.) have. Contributions are made at different stages of the visual analytics pipeline, including novel ways to visualize salient temporal accumulations for further exploration. Moreover, based on the visualization, an automatic algorithm aims to detect and preselect interesting time interval patterns for different features in order to guide analysts. The main target group for the suggested methods are business analysts who want to explore time-stamped customer feedback to detect critical issues. Finally, application case studies on two different datasets and scenarios are conducted and an extensive evaluation is provided for the presented intelligent visual interface for feature-based sentiment exploration over time.<\/jats:p>","DOI":"10.1145\/2089094.2089102","type":"journal-article","created":{"date-parts":[[2012,10,12]],"date-time":"2012-10-12T20:56:02Z","timestamp":1350075362000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["Feature-Based Visual Sentiment Analysis of Text Document Streams"],"prefix":"10.1145","volume":"3","author":[{"given":"Christian","family":"Rohrdantz","sequence":"first","affiliation":[{"name":"University of Konstanz"}]},{"given":"Ming C.","family":"Hao","sequence":"additional","affiliation":[{"name":"Hewlett Packard Labs"}]},{"given":"Umeshwar","family":"Dayal","sequence":"additional","affiliation":[{"name":"Hewlett Packard Labs"}]},{"given":"Lars-Erik","family":"Haug","sequence":"additional","affiliation":[{"name":"Hewlett Packard Labs"}]},{"given":"Daniel A.","family":"Keim","sequence":"additional","affiliation":[{"name":"University of Konstanz"}]}],"member":"320","published-online":{"date-parts":[[2012,2]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cag.2007.01.030"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/11555261_66"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1277741.1277921"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1341531.1341561"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8659.2008.01222.x"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/2945.981848"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.3115\/1220355.1220555"},{"key":"e_1_2_1_8_1","volume-title":"Proceedings of the Computational Linguistics & Applications Conference (CLA&rsquo;\u201910)","author":"Kisilevich S.","unstructured":"Kisilevich , S. , Rohrdantz , C. , and Keim , D. 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