{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T22:28:09Z","timestamp":1768343289334,"version":"3.49.0"},"reference-count":67,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2018,8,20]],"date-time":"2018-08-20T00:00:00Z","timestamp":1534723200000},"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":[[2018,8,20]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>The purpose of this paper is to propose a framework for intelligent analysis of Twitter data. The purpose of the framework is to allow users to explore a collection of tweets by extracting topics with semantic relevance. In this way, it is possible to detect groups of tweets related to new technologies, events and other topics that are automatically discovered.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>The framework is based on a three-stage process. The first stage is devoted to dataset creation by transforming a collection of tweets in a dataset according to the vector space model. The second stage, which is the core of the framework, is centered on the use of non-negative matrix factorizations (NMF) for extracting human-interpretable topics from tweets that are eventually clustered. The number of topics can be user-defined or can be discovered automatically by applying subtractive clustering as a preliminary step before factorization. Cluster analysis and word-cloud visualization are used in the last stage to enable intelligent data analysis.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>The authors applied the framework to a case study of three collections of Italian tweets both with manual and automatic selection of the number of topics. Given the high sparsity of Twitter data, the authors also investigated the influence of different initializations mechanisms for NMF on the factorization results. Numerical comparisons confirm that NMF could be used for clustering as it is comparable to classical clustering techniques such as spherical k-means. Visual inspection of the word-clouds allowed a qualitative assessment of the results that confirmed the expected outcomes.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>The proposed framework enables a collaborative approach between users and computers for an intelligent analysis of Twitter data. Users are faced with interpretable descriptions of tweet clusters, which can be interactively refined with few adjustable parameters. The resulting clusters can be used for intelligent selection of tweets, as well as for further analytics concerning the impact of products, events, etc. in the social network.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ijwis-11-2017-0081","type":"journal-article","created":{"date-parts":[[2018,7,23]],"date-time":"2018-07-23T19:09:36Z","timestamp":1532372976000},"page":"334-356","source":"Crossref","is-referenced-by-count":21,"title":["A framework for intelligent Twitter data analysis with non-negative matrix factorization"],"prefix":"10.1108","volume":"14","author":[{"given":"Gabriella","family":"Casalino","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ciro","family":"Castiello","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicoletta","family":"Del Buono","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Corrado","family":"Mencar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"key":"key2020092718122995200_ref001","volume-title":"Algorithms, initializations, and convergence for the nonnegative matrix factorization","year":"2006"},{"key":"key2020092718122995200_ref002","first-page":"219","volume-title":"Interpretability of Fuzzy Systems: Current Research Trends and Prospects","year":"2015"},{"key":"key2020092718122995200_ref003","unstructured":"Alvari, H. (2017), \u201cTwitter hashtag recommendation using matrix factorization\u201d, CoRR. available at: http:\/\/arxiv.org\/abs\/1705.10453"},{"issue":"9","key":"key2020092718122995200_ref004","first-page":"8","article-title":"Emotion detection of tweets in Indonesian language using non-negative matrix factorization","volume":"6","year":"2014","journal-title":"International Journal of Intelligent Systems and Applications"},{"key":"key2020092718122995200_ref005","first-page":"161","volume-title":"The Classification and Visualization of Twitter Trending Topics considering Time Series Variation","year":"2017"},{"key":"key2020092718122995200_ref006","first-page":"21","article-title":"Ensemble topic modeling via matrix factorization","volume-title":"Proceedings of the 24th Irish Conference on Artificial Intelligence and Cognitive Science, AICS 2016","year":"2016"},{"issue":"1","key":"key2020092718122995200_ref007","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.csda.2006.11.006","article-title":"Algorithms and applications for approximate nonnegative matrix factorization","volume":"52","year":"2007","journal-title":"Computational Statistics and Data Analysis"},{"key":"key2020092718122995200_ref008","volume-title":"Intelligent Data Analysis: An Introduction","year":"1999","edition":"1st"},{"key":"key2020092718122995200_ref009","volume-title":"Guide to Intelligent Data Analysis: How to Intelligently Make Sense of Real Data","year":"2010"},{"key":"key2020092718122995200_ref010","volume-title":"Natural Language Processing with Python","year":"2009"},{"issue":"4","key":"key2020092718122995200_ref011","doi-asserted-by":"crossref","first-page":"1350","DOI":"10.1016\/j.patcog.2007.09.010","article-title":"SVD based initialization: a head start for nonnegative matrix factorization","volume":"41","year":"2008","journal-title":"Pattern Recognition"},{"key":"key2020092718122995200_ref012","first-page":"203","article-title":"Q-matrix extraction from real response data using nonnegative matrix factorizations","volume":"10404","year":"2017","journal-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"key2020092718122995200_ref013","article-title":"Intelligent twitter data analysis based on nonnegative matrix factorizations","volume-title":"Computational Science and Its Applications ICCSA 2017","year":"2017"},{"key":"key2020092718122995200_ref014","first-page":"188","article-title":"Subtractive initialization of nonnegative matrix factorizations for document clustering","volume-title":"Fuzzy Logic and Applications","year":"2011"},{"key":"key2020092718122995200_ref015","first-page":"440","article-title":"Part-based data analysis with masked non-negative matrix factorization","volume-title":"Computational Science and Its Applications - ICCSA 2014 - 14th International Conference, Guimar\u00e3es, Portugal, June 30 - July 3, 2014, Proceedings, Part VI","year":"2014"},{"key":"key2020092718122995200_ref016","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1016\/j.ins.2013.05.038","article-title":"Subtractive clustering for seeding non-negative matrix factorizations","volume":"257","year":"2014","journal-title":"Information Sciences"},{"key":"key2020092718122995200_ref017","first-page":"49","volume-title":"Nonnegative Matrix Factorizations for Intelligent Data Analysis","year":"2016"},{"key":"key2020092718122995200_ref018","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.patcog.2016.09.006","article-title":"Sequential dimensionality reduction for extracting localized features","volume":"63","year":"2017","journal-title":"Pattern Recognition"},{"issue":"10","key":"key2020092718122995200_ref019","doi-asserted-by":"crossref","first-page":"1459","DOI":"10.1109\/TKDE.2009.169","article-title":"Non-negative matrix factorization for semisupervised heterogeneous data coclustering","volume":"22","year":"2010","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"key2020092718122995200_ref020","volume-title":"Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-Way Data Analysis and Blind Source Separation","year":"2009"},{"issue":"4","key":"key2020092718122995200_ref021","doi-asserted-by":"crossref","first-page":"2269","DOI":"10.1109\/TITS.2015.2404431","article-title":"Real-time detection of traffic from twitter stream analysis","volume":"16","year":"2015","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"6","key":"key2020092718122995200_ref022","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":"key2020092718122995200_ref023","first-page":"281","volume-title":"Breast Cancer\u2019s Microarray Data: Pattern Discovery Using Nonnegative Matrix Factorizations","year":"2016"},{"key":"key2020092718122995200_ref024","first-page":"606","article-title":"On the equivalence of nonnegative matrix factorization and k-means - spectral clustering","volume-title":"Proceedings of the SIAM Data Mining Conference","year":"2005"},{"key":"key2020092718122995200_ref025","first-page":"1","volume-title":"A Novel Approach for Internet Traffic Classification Based on Multi-Objective Evolutionary Fuzzy Classifiers","year":"2017"},{"key":"key2020092718122995200_ref026","first-page":"39","volume-title":"Finding Hierarchy of Topics from Twitter Data, Lernen, Wissen, Daten, Analysen (LWDA) Conference Proceedings","year":"2017"},{"key":"key2020092718122995200_ref027","first-page":"3349","article-title":"Sparse and unique nonnegative matrix factorization through data preprocessing","volume":"13","year":"2012","journal-title":"Journal of Machine Learning Research"},{"key":"key2020092718122995200_ref028","article-title":"The why and how of nonnegative matrix factorization","volume-title":"Regularization, Optimization, Kernels, and Support Vector Machines","year":"2014"},{"key":"key2020092718122995200_ref029","unstructured":"Godfrey, D. Johns, C. Sadek, C. Meyer, C. and Race, S. (2014), \u201cA case study in text mining: Interpreting twitter data from world cup tweets\u201d, available at: https:\/\/arxiv.org\/pdf\/1408.5427.pdfl"},{"key":"key2020092718122995200_ref030","article-title":"Mining hot topics from twitter streams, Procedia Computer Science 9(Supplement C): 2008 \u2013 2011","year":"2012"},{"key":"key2020092718122995200_ref031","first-page":"23","year":"2012"},{"issue":"6","key":"key2020092718122995200_ref032","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1016\/j.jbi.2007.10.001","article-title":"Intelligent data analysis in biomedicine","volume":"40","year":"2007","journal-title":"Journal of Biomedical Informatics"},{"key":"key2020092718122995200_ref033","doi-asserted-by":"crossref","unstructured":"Ibrahim, R. Elbagoury, A. Kamel, M.S. and Karray, F. (2017), \u201cTools and approaches for topic detection from twitter streams: survey, knowledge and information systems\u201d, available at: https:\/\/doi.org\/10.1007\/s10115-017-1081-x","DOI":"10.1007\/s10115-017-1081-x"},{"issue":"1","key":"key2020092718122995200_ref034","first-page":"020010","article-title":"Topic extraction method using RED-NMF algorithm for detecting outbreak of some disease on twitter","volume":"1825","year":"2017","journal-title":"AIP Conference Proceedings"},{"issue":"9","key":"key2020092718122995200_ref035","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1109\/MCOM.2013.6588663","article-title":"Understanding user behavior in online social networks: a survey","volume":"51","year":"2013","journal-title":"Communications Magazine, IEEE"},{"issue":"12","key":"key2020092718122995200_ref036","doi-asserted-by":"crossref","first-page":"1495","DOI":"10.1093\/bioinformatics\/btm134","article-title":"Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis","volume":"23","year":"2007","journal-title":"Bioinformatics (Oxford, England)"},{"key":"key2020092718122995200_ref037","first-page":"8","article-title":"Two applications of clustering techniques to twitter: Community detection and issue extraction","volume":"2013","year":"2013","journal-title":"Discrete Dynamics in Nature and Society"},{"key":"key2020092718122995200_ref038","first-page":"1","volume-title":"A Study on Topics Identification on Twitter Using Clustering Algorithms","year":"2015"},{"key":"key2020092718122995200_ref039","volume-title":"Comparison of Clustering Algorithms for the Identification of Topics on Twitter","year":"2016"},{"key":"key2020092718122995200_ref040","volume-title":"Nonnegative Matrix Factorization for Interactive Topic Modeling and Document Clustering","year":"2015"},{"issue":"3","key":"key2020092718122995200_ref041","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1093\/imamat\/hxw025","article-title":"Topic time series analysis of microblogs","volume":"81","year":"2016","journal-title":"IMA Journal of Applied Mathematics"},{"issue":"6755","key":"key2020092718122995200_ref042","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1038\/44565","article-title":"Learning the parts of objects by non-negative matrix factorization","volume":"401","year":"1999","journal-title":"Nature"},{"key":"key2020092718122995200_ref043","first-page":"556","article-title":"Algorithms for non-negative matrix factorization","volume-title":"Advances in Neural Information Processing Systems 13","year":"2001"},{"key":"key2020092718122995200_ref044","year":"2015"},{"issue":"10","key":"key2020092718122995200_ref045","doi-asserted-by":"crossref","first-page":"2756","DOI":"10.1162\/neco.2007.19.10.2756","article-title":"Projected gradient methods for nonnegative matrix factorization","volume":"19","year":"2007","journal-title":"Neural Comput"},{"key":"key2020092718122995200_ref046","volume-title":"Computational Methods of Feature Selection (Chapman and Hall\/Crc Data Mining and Knowledge Discovery Series)","year":"2007"},{"issue":"4","key":"key2020092718122995200_ref047","doi-asserted-by":"crossref","first-page":"1950","DOI":"10.1214\/15-AOAS858","article-title":"Analysis of multiview legislative networks with structured matrix factorization: Does twitter influence translate to the real world?","volume":"9","year":"2015","journal-title":"The Annals of Applied Statistics"},{"key":"key2020092718122995200_ref048","article-title":"Twitter as a corpus for sentiment analysis and opinion mining","volume-title":"Proceedings of the Seventh International Conference on Language Resources and Evaluation: LREC 2010","year":"2010"},{"key":"key2020092718122995200_ref049","first-page":"3","year":"2014"},{"issue":"3","key":"key2020092718122995200_ref050","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1109\/TPAMI.2006.60","article-title":"Nonsmooth nonnegative matrix factorization (NSNMF)","volume":"28","year":"2006","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"key2020092718122995200_ref051","first-page":"2083","year":"2015"},{"key":"key2020092718122995200_ref052","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","article-title":"Silhouettes: a graphical aid to the interpretation and validation of cluster analysis","volume":"20","year":"1987","journal-title":"Journal of Computational and Applied Mathematics"},{"key":"key2020092718122995200_ref053","first-page":"693","year":"2012"},{"issue":"9","key":"key2020092718122995200_ref054","first-page":"1","article-title":"Tracking time evolution of collective attention clusters in twitter: Time evolving nonnegative matrix factorisation","volume":"10","year":"2015","journal-title":"Plos One"},{"issue":"11","key":"key2020092718122995200_ref055","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1145\/361219.361220","article-title":"A vector space model for automatic indexing","volume":"18","year":"1975","journal-title":"Communications of the ACM"},{"issue":"8","key":"key2020092718122995200_ref056","first-page":"1","article-title":"The successive projection algorithm as an initialization method for brain tumor segmentation using non-negative matrix factorization","volume":"12","year":"2017","journal-title":"Plos One"},{"issue":"2","key":"key2020092718122995200_ref057","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.ipm.2004.11.005","article-title":"Document clustering using nonnegative matrix factorization","volume":"42","year":"2006","journal-title":"Inf. Process. Manage"},{"key":"key2020092718122995200_ref058","first-page":"3","year":"2009"},{"key":"key2020092718122995200_ref059","first-page":"435","year":"2017"},{"issue":"1","key":"key2020092718122995200_ref060","doi-asserted-by":"crossref","first-page":"330","DOI":"10.11591\/ijece.v7i1.pp330-336","article-title":"Sensing trending topics in twitter for greater Jakarta area","volume":"7","year":"2017","journal-title":"International Journal of Electrical and Computer Engineering (IJECE))"},{"key":"key2020092718122995200_ref061","first-page":"479","year":"2016"},{"key":"key2020092718122995200_ref062","first-page":"4944","year":"2017"},{"key":"key2020092718122995200_ref063","first-page":"1","year":"2017"},{"key":"key2020092718122995200_ref064","first-page":"128","year":"2015"},{"issue":"8","key":"key2020092718122995200_ref065","doi-asserted-by":"crossref","first-page":"2158","DOI":"10.1109\/TKDE.2016.2553667","article-title":"Quantifying political leaning from tweets, retweets, and retweeters","volume":"28","year":"2016","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"key2020092718122995200_ref066","first-page":"267","year":"2003"},{"key":"key2020092718122995200_ref067","first-page":"749","year":"2018","journal-title":"Learning Topics in Short Texts by Non-Negative Matrix Factorization on Term Correlation Matrix"}],"container-title":["International Journal of Web Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJWIS-11-2017-0081\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJWIS-11-2017-0081\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T22:24:24Z","timestamp":1753395864000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/ijwis\/article\/14\/3\/334-356\/166996"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,20]]},"references-count":67,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2018,8,20]]}},"alternative-id":["10.1108\/IJWIS-11-2017-0081"],"URL":"https:\/\/doi.org\/10.1108\/ijwis-11-2017-0081","relation":{},"ISSN":["1744-0084"],"issn-type":[{"value":"1744-0084","type":"print"}],"subject":[],"published":{"date-parts":[[2018,8,20]]}}}