{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T07:32:20Z","timestamp":1763105540102,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319672557"},{"type":"electronic","value":"9783319672564"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1007\/978-3-319-67256-4_26","type":"book-chapter","created":{"date-parts":[[2017,9,1]],"date-time":"2017-09-01T14:45:33Z","timestamp":1504277133000},"page":"329-346","source":"Crossref","is-referenced-by-count":4,"title":["Twigraph: Discovering and Visualizing Influential Words Between Twitter Profiles"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8192-8155","authenticated-orcid":false,"given":"Dhanasekar","family":"Sundararaman","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2103-7825","authenticated-orcid":false,"given":"Sudharshan","family":"Srinivasan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,9,2]]},"reference":[{"key":"26_CR1","doi-asserted-by":"crossref","unstructured":"Java, A., et al.: Why we twitter: understanding microblogging usage and communities. In: Proceedings of the 9thWebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis. ACM (2007)","DOI":"10.1145\/1348549.1348556"},{"key":"26_CR2","first-page":"2010","volume":"10","author":"A Pak","year":"2010","unstructured":"Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. LREc 10, 2010 (2010)","journal-title":"LREc"},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"Gupta, P., et al.: WTF: The who to follow service at twitter. In: Proceedings of the 22nd International Conference on World Wide Web. ACM (2013)","DOI":"10.1145\/2488388.2488433"},{"key":"26_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"784","DOI":"10.1007\/978-3-642-20161-5_94","volume-title":"Advances in Information Retrieval","author":"J Hannon","year":"2011","unstructured":"Hannon, J., McCarthy, K., Smyth, B.: Finding useful users on twitter: twittomender the followee recommender. In: Clough, P., Foley, C., Gurrin, C., Jones, Gareth J.F., Kraaij, W., Lee, H., Mudoch, V. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 784\u2013787. Springer, Heidelberg (2011). doi: 10.1007\/978-3-642-20161-5_94"},{"issue":"1","key":"26_CR5","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1109\/MIS.2015.16","volume":"30","author":"V Kagan","year":"2015","unstructured":"Kagan, V., Stevens, A., Subrahmanian, V.S.: Using twitter sentiment to forecast the 2013 pakistani election and the 2014 indian election. IEEE Intell. Syst. 30(1), 2\u20135 (2015)","journal-title":"IEEE Intell. Syst."},{"key":"26_CR6","doi-asserted-by":"crossref","unstructured":"Tunggawan, E., Soelistio, Y.E.: And the Winner is\u2026: Bayesian Twitter-based Prediction on 2016 US Presidential Election. arXiv preprint arXiv:1611.00440 (2016)","DOI":"10.1109\/IC3INA.2016.7863019"},{"key":"26_CR7","unstructured":"Ramos, J.: Using TF-IDF to determine word relevance in document queries. In: Proceedings of the First Instructional Conference on Machine Learning (2003)"},{"key":"26_CR8","doi-asserted-by":"crossref","unstructured":"Jing, L.-P., Huang, H.-K., Shi, H.-B.: Improved feature selection approach TFIDF in text mining. In: Proceedings of 2002 International Conference on Machine Learning and Cybernetics, vol. 2. IEEE (2002)","DOI":"10.1109\/ICMLC.2002.1174522"},{"key":"26_CR9","unstructured":"Huang, A.: Similarity measures for text document clustering. In: Proceedings of the Sixth New Zealand Computer Science Research Student Conference (NZCSRSC 2008), Christchurch, New Zealand (2008)"},{"key":"26_CR10","unstructured":"Steinbach, M., Karypis, G., Kumar, V.: A comparison of document clustering techniques. In: KDD Workshop on Text Mining, vol. 400(1) (2000)"},{"issue":"5","key":"26_CR11","first-page":"30","volume":"4","author":"N Shah","year":"2012","unstructured":"Shah, N., Mahajan, S.: Document clustering: a detailed review. Int. J. Appl. Inf. Syst. 4(5), 30\u201338 (2012)","journal-title":"Int. J. Appl. Inf. Syst."},{"key":"26_CR12","doi-asserted-by":"crossref","unstructured":"Cutting, D.R., et al.: Scatter\/gather: a clusterbased approach to browsing large document collections. In: Proceedings of the 15th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM (1992)","DOI":"10.1145\/133160.133214"},{"key":"26_CR13","doi-asserted-by":"crossref","unstructured":"Bhaumik, H., et al.: Towards reliable clustering of english text documents using correlation coefficient. In: 2014 International Conference on Computational Intelligence and Communication Networks (CICN). IEEE (2014)","DOI":"10.1109\/CICN.2014.121"},{"key":"26_CR14","unstructured":"Li, G., Liu, F.: A clustering-based approach on sentiment analysis. In: 2010 International Conference on Intelligent Systems and Knowledge Engineering (ISKE). IEEE (2010)"},{"key":"26_CR15","doi-asserted-by":"publisher","unstructured":"Kavyasrujana, D., Rao, B.C.: Hierarchical clustering for sentence extraction using cosine similarity measure. In: Satapathy, S., Govardhan, A., Raju, K., Mandal, J. (eds.) Emerging ICT for Bridging the Future - Proceedings of the 49th Annual Convention of the Computer Society of India (CSI) Volume 1. AISC, vol. 337, pp. 185\u2013191. Springer, Cham (2015). doi: 10.1007\/978-3-319-13728-5_21","DOI":"10.1007\/978-3-319-13728-5_21"},{"issue":"6","key":"26_CR16","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1016\/j.ipm.2003.10.006","volume":"40","author":"DR Radev","year":"2004","unstructured":"Radev, D.R., et al.: Centroid-based summarization of multiple documents. Inf. Process. Manage. 40(6), 919\u2013938 (2004)","journal-title":"Inf. Process. Manage."},{"key":"26_CR17","unstructured":"Mihalcea, R., Corley, C., Strapparava, C.: Corpus-based and knowledge-based measures of text semantic similarity.In: AAAI, vol. 6 (2006)"},{"key":"26_CR18","unstructured":"Tweepy, https:\/\/github.com\/tweepy\/tweepy"},{"key":"26_CR19","unstructured":"Reuters Institute for the Study of Journalism. Digital news report 2015: Tracking the future of news (2015). http:\/\/www.digitalnewsreport.org\/survey\/2015\/socialnetworks-and-their-role-in-news-2015\/"},{"key":"26_CR20","unstructured":"Pew Research Center. The evolving role of news on twitter and facebook (2015). http:\/\/www.journalism.org\/2015\/07\/14\/the-evolving-role-of-news-ontwitter-and-facebook"},{"key":"26_CR21","unstructured":"Twigraph Source code. https:\/\/github.com\/Dhanasekar-S\/Twigraph_Source_Code"}],"container-title":["Lecture Notes in Computer Science","Social Informatics"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-67256-4_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,2]],"date-time":"2019-10-02T22:01:20Z","timestamp":1570053680000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-67256-4_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9783319672557","9783319672564"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-67256-4_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2017]]}}}