{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T16:38:14Z","timestamp":1777999094489,"version":"3.51.4"},"reference-count":19,"publisher":"Emerald","issue":"5","license":[{"start":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T00:00:00Z","timestamp":1602460800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJWIS"],"published-print":{"date-parts":[[2020,10,12]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>Social media has become a vital part of any institute\u2019s marketing plan. Social networks benefit businesses by allowing them to interact with their clients, grow brand exposure through offers and promotions and find new leads. It also offers vital information concerning the general emotions and sentiments directly connected to the welfare and security of the online community involved with the brand. Big organizations can make use of their social media data to generate planned and operational decisions. This paper aims to look into the conversion of sentiments and emotions over time.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>In this work, a model called sentiment urgency emotion detection (SUED) from previous work will be applied on tweets from two different periods of time, one before the start of the COVID-19 pandemic and the other after it started to monitor the conversion of sentiments and emotions over time. The model has been trained to improve its accuracy and F1 score so that the precision and percentage of correctly predicted texts is high. This model will be tuned to improve results (Soussan and Trovati, 2020a; Soussan and Trovati, 2020b) and will be applied on a general business Twitter account of one of the largest chains of supermarkets in the UK to be able to see what sentiments and emotions can be detected and how urgent they are.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>This will show the effect of COVID-19 pandemic on the conversions of the sentiments, emotions and urgencies of the tweets.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>Sentiments will be compared between the two periods to evaluate how sentiments and emotions vary over time taking into consideration the COVID-19 as an affective factor. In addition, SUED will be tuned to enhance results and the knowledge that is mined when turning data into decisions is crucial because it will aid stakeholders handling the institute to evaluate the topics and issues that were mostly emphasized.<\/jats:p><\/jats:sec>","DOI":"10.1108\/ijwis-08-2020-0049","type":"journal-article","created":{"date-parts":[[2020,10,9]],"date-time":"2020-10-09T01:36:17Z","timestamp":1602207377000},"page":"519-528","source":"Crossref","is-referenced-by-count":5,"title":["Sentiment urgency emotion conversion over time for business intelligence"],"prefix":"10.1108","volume":"16","author":[{"given":"Tariq","family":"Soussan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marcello","family":"Trovati","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"key":"key2020110603180584800_ref001","first-page":"30","article-title":"Sentiment analysis of twitter data","volume-title":"Proceedings of the workshop on language in social media (LSM 2011)","year":"2011"},{"key":"key2020110603180584800_ref002","article-title":"U.S. Patent No. 8,515,049","year":"2013"},{"key":"key2020110603180584800_ref003","article-title":"A literature review on the topic of social media, file retrieved from blog post","year":"2010"},{"issue":"1","key":"key2020110603180584800_ref004","first-page":"5","article-title":"An empirical analysis of the educational effects of social media in universities and colleges","volume":"2","year":"2013","journal-title":"Internet Learning"},{"issue":"11","key":"key2020110603180584800_ref005","doi-asserted-by":"crossref","first-page":"2169","DOI":"10.1002\/asi.21149","article-title":"Twitter power: tweets as electronic word of mouth","volume":"60","year":"2009","journal-title":"Journal of the American Society for Information Science and Technology"},{"key":"key2020110603180584800_ref006","first-page":"1430","article-title":"Real-time emotion classification of tweets","volume-title":"2013 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","year":"2013"},{"issue":"12","key":"key2020110603180584800_ref007","first-page":"83","article-title":"Social media strategy: follow the 6 P's for successful outreach","volume":"27","year":"2011","journal-title":"Alaska Business Monthly"},{"issue":"1","key":"key2020110603180584800_ref008","first-page":"2074","article-title":"Text opinion mining to analyze news for stock market prediction","volume":"6","year":"2014","journal-title":"Int. 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