{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,15]],"date-time":"2026-02-15T21:27:04Z","timestamp":1771190824965,"version":"3.50.1"},"reference-count":20,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T00:00:00Z","timestamp":1612137600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJICC"],"published-print":{"date-parts":[[2021,4,23]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>The purpose of the paper is to study multiple viewpoints which are required to access the more informative similarity features among the tweets documents, which is useful for achieving the robust tweets data clustering results.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>Let \u201c<jats:italic>N<\/jats:italic>\u201d be the number of tweets documents for the topics extraction. Unwanted texts, punctuations and other symbols are removed, tokenization and stemming operations are performed in the initial tweets pre-processing step. Bag-of-features are determined for the tweets; later tweets are modelled with the obtained bag-of-features during the process of topics extraction. Approximation of topics features are extracted for every tweet document. These set of topics features of <jats:italic>N<\/jats:italic> documents are treated as multi-viewpoints. The key idea of the proposed work is to use multi-viewpoints in the similarity features computation. The following figure illustrates multi-viewpoints based cosine similarity computation of the five tweets documents (here <jats:italic>N<\/jats:italic>\u00a0=\u00a05) and corresponding documents are defined in projected space with five viewpoints, say, <jats:italic>v<\/jats:italic><jats:sub>1<\/jats:sub>,<jats:italic>v<\/jats:italic><jats:sub>2<\/jats:sub>, <jats:italic>v<\/jats:italic><jats:sub>3<\/jats:sub>, <jats:italic>v<\/jats:italic><jats:sub>4<\/jats:sub>, and <jats:italic>v<\/jats:italic><jats:sub>5<\/jats:sub>. For example, similarity features between two documents (viewpoints <jats:italic>v<\/jats:italic><jats:sub>1<\/jats:sub>, and <jats:italic>v<\/jats:italic><jats:sub>2<\/jats:sub>) are computed concerning the other three multi-viewpoints (<jats:italic>v<\/jats:italic><jats:sub>3<\/jats:sub>, <jats:italic>v<\/jats:italic><jats:sub>4<\/jats:sub>, and <jats:italic>v<\/jats:italic><jats:sub>5<\/jats:sub>), unlike a single viewpoint in traditional cosine metric.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>Healthcare problems with tweets data. Topic models play a crucial role in the classification of health-related tweets with finding topics (or health clusters) instead of finding term frequency and inverse document frequency (TF\u2013IDF) for unlabelled tweets.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>Topic models play a crucial role in the classification of health-related tweets with finding topics (or health clusters) instead of finding TF-IDF for unlabelled tweets.<\/jats:p><\/jats:sec>","DOI":"10.1108\/ijicc-10-2020-0151","type":"journal-article","created":{"date-parts":[[2021,2,8]],"date-time":"2021-02-08T20:52:49Z","timestamp":1612817569000},"page":"170-184","source":"Crossref","is-referenced-by-count":11,"title":["An enhanced cosine-based visual technique for the robust tweets data clustering"],"prefix":"10.1108","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0756-379X","authenticated-orcid":false,"given":"Narasimhulu","family":"K","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meena Abarna","family":"KT","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sivakumar","family":"B","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2021,2,1]]},"reference":[{"key":"key2022121313515098700_ref001","article-title":"Is normalized mutual information a fair measure for comparing community detection methods?","year":"2015"},{"issue":"11","key":"key2022121313515098700_ref002","doi-asserted-by":"crossref","first-page":"2978","DOI":"10.35940\/ijitee.K2285.0981119","article-title":"Cluster tendency methods for visualizing the data partitions","volume":"8","year":"2019","journal-title":"International Journal of Innovative Technology and Exploring Engineering"},{"key":"key2022121313515098700_ref003","first-page":"2225","article-title":"VAT: a tool for visual assessment of (cluster) tendency","year":"2002"},{"key":"key2022121313515098700_ref004","article-title":"SpecVAT: enhanced visual cluster analysis","year":"2008"},{"key":"key2022121313515098700_ref005","first-page":"993","article-title":"Latent Dirichlet allocation","volume":"3","year":"2003","journal-title":"Journal of Machine Learning Research"},{"issue":"6","key":"key2022121313515098700_ref006","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1002\/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9","article-title":"Indexing by latent semantic analysis","volume":"41","year":"1990","journal-title":"Journal of the American Society for Information Science"},{"key":"key2022121313515098700_ref007","doi-asserted-by":"crossref","unstructured":"Hofmann, T. 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