{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:29:49Z","timestamp":1777703389819,"version":"3.51.4"},"reference-count":34,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2015,7,29]],"date-time":"2015-07-29T00:00:00Z","timestamp":1438128000000},"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 Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2015,7,29]]},"abstract":"<jats:p>\n                    Sentiment analysis and polarity detection is a type of text classification where natural language opinion is analyzed in order to classify it into either positive or negative categories. Classification of text into sentiment labels is a very difficult task as opinions expressed in natural language may contain abbreviations, slangs, sarcasm, irony and\/or idioms. The proposed research focuses on the use of SentiWordNet3.0 as a labeled corpus for training purposes. We present a complete framework based on a dictionary named Normalized SentiMI (nSentiMI) which is created by calculating point-wise mutual information for each term\/part-of-speech pair extracted from SentiWordNet. The proposed framework is applied on a dataset of 50,000 movie reviews to identify the value of a weight factor\n                    <jats:italic>\u03b1<\/jats:italic>\n                    and then evaluated on an unseen test dataset of 2000 movie reviews. Comparison with state of art techniques also confirms the superiority of proposed approach.\n                  <\/jats:p>","DOI":"10.3233\/ifs-151658","type":"journal-article","created":{"date-parts":[[2015,11,10]],"date-time":"2015-11-10T11:34:38Z","timestamp":1447155278000},"page":"1805-1816","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":10,"title":["Building Normalized SentiMI to enhance semi-supervised sentiment analysis"],"prefix":"10.1177","volume":"29","author":[{"given":"Farhan","family":"Hassan Khan","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Usman","family":"Qamar","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saba","family":"Bashir","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2015,7,29]]},"reference":[{"key":"e_1_3_1_2_2","article-title":"Sentiment classification of reviews using SentiWordNet","author":"Ohana B","year":"2009","unstructured":"Ohana B, Tierney B 2009 Sentiment classification of reviews using SentiWordNet 9th IT&T Conference, Dublin Institute of Technology Dublin, Ireland","journal-title":"9th IT&T Conference, Dublin Institute of Technology"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8640.2006.00277.x"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1561\/1500000011"},{"key":"e_1_3_1_5_2","article-title":"Sentiwordnet: A publicly available lexical resource for opinion mining","author":"Esuli A","year":"2006","unstructured":"Esuli A, Sebastiani F 2006 Sentiwordnet: A publicly available lexical resource for opinion mining 5th Conference on Language Resources and Evaluation","journal-title":"5th Conference on Language Resources and Evaluation"},{"key":"e_1_3_1_6_2","doi-asserted-by":"crossref","unstructured":"Paltoglou G 2014 Sentiment analysis on social media. 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