{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T18:21:19Z","timestamp":1775326879326,"version":"3.50.1"},"reference-count":15,"publisher":"Wiley","license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Computational Intelligence and Soft Computing"],"published-print":{"date-parts":[[2016]]},"abstract":"<jats:p>The end of the course evaluation has become an integral part of education management in almost every academic institution. The existing automated evaluation method primarily employs the Likert scale based quantitative scores provided by students about the delivery of the course and the knowledge of the instructor. The feedback is subsequently used to improve the quality of the teaching and often for the annual appraisal process. In addition to the Likert scale questions, the evaluation form typically contains open-ended questions where students can write general comments\/feedback that might not be covered by the fixed questions. The textual feedback, however, is usually provided to teachers and administration and due to its nonquantitative nature is frequently not processed to gain more insight. This paper aims to address this aspect by applying several text analytics methods on students\u2019 feedback. The paper not only presents a sentiment analysis based metric, which is shown to be highly correlated with the aggregated Likert scale scores, but also provides new insight into a teacher\u2019s performance with the help of tag clouds, sentiment score, and other frequency-based filters.<\/jats:p>","DOI":"10.1155\/2016\/2385429","type":"journal-article","created":{"date-parts":[[2016,10,13]],"date-time":"2016-10-13T17:12:30Z","timestamp":1476378750000},"page":"1-12","source":"Crossref","is-referenced-by-count":70,"title":["Lexicon-Based Sentiment Analysis of Teachers\u2019 Evaluation"],"prefix":"10.1155","volume":"2016","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0916-9577","authenticated-orcid":true,"given":"Quratulain","family":"Rajput","sequence":"first","affiliation":[{"name":"Faculty of Computer Science, Institute of Business Administration (IBA), Garden\/Kiyani Shaheed Road, Karachi 74400, Pakistan"}]},{"given":"Sajjad","family":"Haider","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Institute of Business Administration (IBA), Garden\/Kiyani Shaheed Road, Karachi 74400, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2938-2789","authenticated-orcid":true,"given":"Sayeed","family":"Ghani","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Institute of Business Administration (IBA), Garden\/Kiyani Shaheed Road, Karachi 74400, Pakistan"}]}],"member":"311","reference":[{"key":"2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2011.08.113"},{"issue":"3","key":"3","first-page":"229","volume":"3","year":"2014","journal-title":"International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1016\/j.asej.2014.04.011"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-07617-1_15"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1016\/j.csl.2013.09.003"},{"key":"12","volume-title":"Sentiment analysis and subjectivity","year":"2010"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2013.30"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1093\/ijl\/3.4.235"},{"key":"18","first-page":"415","volume-title":"A survey of opinion mining and sentiment analysis","year":"2012"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-011-0238-6"},{"key":"22","series-title":"Lecture Notes in Computer Science","first-page":"478","volume-title":"An introduction to concept-level sentiment analysis","year":"2013"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/715730"},{"key":"26","doi-asserted-by":"publisher","DOI":"10.1007\/s10579-005-7880-9"},{"key":"27","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2011.09.076"},{"key":"28","doi-asserted-by":"publisher","DOI":"10.1145\/2701583.2701592"}],"container-title":["Applied Computational Intelligence and Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/acisc\/2016\/2385429.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/acisc\/2016\/2385429.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/acisc\/2016\/2385429.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2016,10,13]],"date-time":"2016-10-13T17:12:31Z","timestamp":1476378751000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/acisc\/2016\/2385429\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"references-count":15,"alternative-id":["2385429","2385429"],"URL":"https:\/\/doi.org\/10.1155\/2016\/2385429","relation":{},"ISSN":["1687-9724","1687-9732"],"issn-type":[{"value":"1687-9724","type":"print"},{"value":"1687-9732","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016]]}}}