{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T02:57:36Z","timestamp":1785466656903,"version":"3.56.0"},"reference-count":45,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2017,4,19]],"date-time":"2017-04-19T00:00:00Z","timestamp":1492560000000},"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":[[2018,8]]},"abstract":"<jats:p>This article introduces a new general-purpose sentiment lexicon called WKWSCI Sentiment Lexicon and compares it with five existing lexicons: Hu &amp; Liu Opinion Lexicon, Multi-perspective Question Answering (MPQA) Subjectivity Lexicon, General Inquirer, National Research Council Canada (NRC) Word-Sentiment Association Lexicon and Semantic Orientation Calculator (SO-CAL) lexicon. The effectiveness of the sentiment lexicons for sentiment categorisation at the document level and sentence level was evaluated using an Amazon product review data set and a news headlines data set. WKWSCI, MPQA, Hu &amp; Liu and SO-CAL lexicons are equally good for product review sentiment categorisation, obtaining accuracy rates of 75%\u201377% when appropriate weights are used for different categories of sentiment words. However, when a training corpus is not available, Hu &amp; Liu obtained the best accuracy with a simple-minded approach of counting positive and negative words for both document-level and sentence-level sentiment categorisation. The WKWSCI lexicon obtained the best accuracy of 69% on the news headlines sentiment categorisation task, and the sentiment strength values obtained a Pearson correlation of 0.57 with human-assigned sentiment values. It is recommended that the Hu &amp; Liu lexicon be used for product review texts and the WKWSCI lexicon for non-review texts.<\/jats:p>","DOI":"10.1177\/0165551517703514","type":"journal-article","created":{"date-parts":[[2017,4,19]],"date-time":"2017-04-19T10:58:12Z","timestamp":1492599492000},"page":"491-511","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":207,"title":["Lexicon-based sentiment analysis: Comparative evaluation of six sentiment lexicons"],"prefix":"10.1177","volume":"44","author":[{"given":"Christopher SG","family":"Khoo","sequence":"first","affiliation":[{"name":"Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sathik Basha","family":"Johnkhan","sequence":"additional","affiliation":[{"name":"Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2017,4,19]]},"reference":[{"key":"bibr1-0165551517703514","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"key":"bibr2-0165551517703514","volume-title":"Statistical learning theory","author":"Vapnik VN","year":"1998"},{"key":"bibr3-0165551517703514","first-page":"562","volume-title":"Proceedings of the seventeenth Florida artificial intelligence research society conference","author":"Zhang H."},{"key":"bibr4-0165551517703514","first-page":"90","volume-title":"Proceedings of the 50th annual meeting of the association for computational linguistics","author":"Wang S"},{"key":"bibr5-0165551517703514","volume-title":"The general inquirer: a computer approach to content analysis","author":"Stone PJ","year":"1966"},{"key":"bibr6-0165551517703514","doi-asserted-by":"publisher","DOI":"10.1162\/COLI_a_00049"},{"key":"bibr7-0165551517703514","first-page":"174","volume-title":"Proceedings of the 35th meeting of the association for computational linguistics","author":"Hatzivassiloglou V"},{"key":"bibr8-0165551517703514","doi-asserted-by":"publisher","DOI":"10.1145\/944012.944013"},{"key":"bibr9-0165551517703514","first-page":"417","volume-title":"Proceedings of the 5th international conference on language resources and evaluation (LREC 2006)","author":"Esuli A"},{"key":"bibr10-0165551517703514","first-page":"38","volume-title":"Proceedings of the 3rd workshop on computational approaches to subjectivity and sentiment analysis","author":"Das A"},{"key":"bibr11-0165551517703514","doi-asserted-by":"publisher","DOI":"10.1002\/asi.22872"},{"key":"bibr12-0165551517703514","doi-asserted-by":"publisher","DOI":"10.1177\/0165551510388123"},{"key":"bibr13-0165551517703514","doi-asserted-by":"publisher","DOI":"10.1007\/s10579-005-7880-9"},{"key":"bibr14-0165551517703514","doi-asserted-by":"publisher","DOI":"10.1108\/14684521211287936"},{"key":"bibr15-0165551517703514","first-page":"70","volume-title":"Proceedings of the 4th international workshop on semantic evaluations","author":"Strapparava C"},{"key":"bibr16-0165551517703514","unstructured":"12 dicts introduction, http:\/\/wordlist.aspell.net\/12dicts-readme\/"},{"key":"bibr17-0165551517703514","first-page":"82","volume-title":"Proceedings of the 17th international conference on Asia-Pacific digital libraries","author":"Khoo CSG"},{"key":"bibr18-0165551517703514","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8640.2012.00460.x"},{"key":"bibr19-0165551517703514","doi-asserted-by":"crossref","unstructured":"Hong Y, Kwak H, Baek Y, Tower of babel: a crowdsourcing game building sentiment lexicons for resource-scarce languages. 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