{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T22:46:50Z","timestamp":1781563610788,"version":"3.54.5"},"reference-count":47,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2019,3,18]],"date-time":"2019-03-18T00:00:00Z","timestamp":1552867200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Information Science"],"published-print":{"date-parts":[[2020,6]]},"abstract":"<jats:p>Sentiment analysis is one of the natural language processing tasks used to find reviews expressed in online texts and classify them into different classes. One of the most important factors affecting the efficiency of sentiment analysis methods is the aggregation algorithm used for scores combination. Recently, Dempster\u2013Shafer algorithm has been used for scores aggregation. This algorithm has a higher precision than common methods such as average, weighed average, product and voting, but the problem with this algorithm is the aggregation of a dominant high or low score that is always selected by the algorithm as the overall score. In the current research, a new method is proposed for scores aggregation that employs both the most and the second probable classes to predict the final score. The proposed approach considers every review as a set of sentences each of which has its own sentiment orientation and score and computes the probability of belonging of every sentence to different classes in a five-star scale using a pure lexicon-based system. These probabilities are then used for document-level sentiment detection. To this aim, two-point structure is used to improve the Dempster\u2013Shafer aggregation algorithm. The proposed method is applied to review datasets of TripAdvisor and CitySearch which have been used in previous studies. The obtained results show that in comparison with the original Dempster\u2013Shafer aggregation method, the precision of the proposed method for both datasets is 23% and 27% higher, respectively.<\/jats:p>","DOI":"10.1177\/0165551519837187","type":"journal-article","created":{"date-parts":[[2019,3,18]],"date-time":"2019-03-18T04:56:47Z","timestamp":1552885007000},"page":"340-360","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":21,"title":["An improved evidence-based aggregation method for sentiment analysis"],"prefix":"10.1177","volume":"46","author":[{"given":"Parisa","family":"Jamadi Khiabani","sequence":"first","affiliation":[{"name":"Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2893-3892","authenticated-orcid":false,"given":"Mohammad Ehsan","family":"Basiri","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Shahrekord University, Shahrekord, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamid","family":"Rastegari","sequence":"additional","affiliation":[{"name":"Faculty of Computer Engineering, Islamic Azad University, Najafabad, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2019,3,18]]},"reference":[{"key":"bibr1-0165551519837187","first-page":"415","volume-title":"Mining text data","author":"Liu B"},{"key":"bibr2-0165551519837187","doi-asserted-by":"publisher","DOI":"10.17705\/1thci.00086"},{"key":"bibr3-0165551519837187","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2012.02.166"},{"key":"bibr4-0165551519837187","doi-asserted-by":"publisher","DOI":"10.2200\/S00416ED1V01Y201204HLT016"},{"key":"bibr5-0165551519837187","doi-asserted-by":"publisher","DOI":"10.1177\/0165551514522734"},{"key":"bibr6-0165551519837187","doi-asserted-by":"publisher","DOI":"10.1145\/3195633"},{"key":"bibr7-0165551519837187","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2015.06.015"},{"key":"bibr8-0165551519837187","first-page":"1","volume":"1","author":"Basiri M","year":"2014","journal-title":"Open Trans Inf Process"},{"key":"bibr9-0165551519837187","doi-asserted-by":"publisher","DOI":"10.1109\/PRIA.2017.7983023"},{"key":"bibr10-0165551519837187","volume-title":"Proceedings of the international symposium on computer science and software engineering conference","author":"Basiri ME"},{"key":"bibr11-0165551519837187","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2017.01.028"},{"key":"bibr12-0165551519837187","doi-asserted-by":"publisher","DOI":"10.1002\/asi.21662"},{"key":"bibr13-0165551519837187","first-page":"1","volume-title":"Computational linguistics and intelligent text processing","author":"Thelwall M"},{"key":"bibr14-0165551519837187","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2016.05.040"},{"key":"bibr15-0165551519837187","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2017.02.028"},{"key":"bibr16-0165551519837187","doi-asserted-by":"publisher","DOI":"10.1155\/2014\/361201"},{"key":"bibr17-0165551519837187","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2017.4531228"},{"key":"bibr18-0165551519837187","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2015.2485209"},{"key":"bibr19-0165551519837187","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2008.06.002"},{"key":"bibr20-0165551519837187","doi-asserted-by":"publisher","DOI":"10.1515\/9780691214696"},{"key":"bibr21-0165551519837187","doi-asserted-by":"publisher","DOI":"10.2174\/9781681085029117010009"},{"key":"bibr22-0165551519837187","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139084789"},{"key":"bibr23-0165551519837187","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2012.03.001"},{"key":"bibr24-0165551519837187","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.psych.54.101601.145041"},{"key":"bibr25-0165551519837187","unstructured":"Ferguson P, O\u2019Hare N, Davy M, et al. 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