A keyword extraction method from twitter messages represented as graphs
Twitter is a microblog service that generates a huge amount of textual content daily. All this content needs to be explored by means of text mining, natural language processing, information retrieval, and other techniques. In this context, automatic keyword extraction is a task of great usefulness....
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| Veröffentlicht in: | Applied mathematics and computation Jg. 240; S. 308 - 325 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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01.08.2014
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| ISSN: | 0096-3003, 1873-5649 |
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| Abstract | Twitter is a microblog service that generates a huge amount of textual content daily. All this content needs to be explored by means of text mining, natural language processing, information retrieval, and other techniques. In this context, automatic keyword extraction is a task of great usefulness. A fundamental step in text mining techniques consists of building a model for text representation. The model known as vector space model, VSM, is the most well-known and used among these techniques. However, some difficulties and limitations of VSM, such as scalability and sparsity, motivate the proposal of alternative approaches. This paper proposes a keyword extraction method for tweet collections that represents texts as graphs and applies centrality measures for finding the relevant vertices (keywords). To assess the performance of the proposed approach, three different sets of experiments are performed. The first experiment applies TKG to a text from the Time magazine and compares its performance with that of the literature. The second set of experiments takes tweets from three different TV shows, applies TKG and compares it with TFIDF and KEA, having human classifications as benchmarks. Finally, these three algorithms are applied to tweets sets of increasing size and their computational running time is measured and compared. Altogether, these experiments provide a general overview of how TKG can be used in practice, its performance when compared with other standard approaches, and how it scales to larger data instances. The results show that TKG is a novel and robust proposal to extract keywords from texts, particularly from short messages, such as tweets. |
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| AbstractList | Twitter is a microblog service that generates a huge amount of textual content daily. All this content needs to be explored by means of text mining, natural language processing, information retrieval, and other techniques. In this context, automatic keyword extraction is a task of great usefulness. A fundamental step in text mining techniques consists of building a model for text representation. The model known as vector space model, VSM, is the most well-known and used among these techniques. However, some difficulties and limitations of VSM, such as scalability and sparsity, motivate the proposal of alternative approaches. This paper proposes a keyword extraction method for tweet collections that represents texts as graphs and applies centrality measures for finding the relevant vertices (keywords). To assess the performance of the proposed approach, three different sets of experiments are performed. The first experiment applies TKG to a text from the Time magazine and compares its performance with that of the literature. The second set of experiments takes tweets from three different TV shows, applies TKG and compares it with TFIDF and KEA, having human classifications as benchmarks. Finally, these three algorithms are applied to tweets sets of increasing size and their computational running time is measured and compared. Altogether, these experiments provide a general overview of how TKG can be used in practice, its performance when compared with other standard approaches, and how it scales to larger data instances. The results show that TKG is a novel and robust proposal to extract keywords from texts, particularly from short messages, such as tweets. |
| Author | de Castro, Leandro N. Abilhoa, Willyan D. |
| Author_xml | – sequence: 1 givenname: Willyan D. surname: Abilhoa fullname: Abilhoa, Willyan D. email: abilhoa.willyan@gmail.com – sequence: 2 givenname: Leandro N. surname: de Castro fullname: de Castro, Leandro N. email: lnunes@mackenzie.br |
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| Cites_doi | 10.1109/ICPP.2010.66 10.3115/1614038.1614047 10.1007/11775300_8 10.1109/NLPKE.2010.5587861 10.1016/j.bushor.2009.09.003 10.3390/ijerph7020596 10.1109/ADL.1998.670375 10.1145/1341531.1341557 10.1093/bib/6.1.57 10.1017/CBO9780511546914 10.1109/WI-IAT.2010.63 10.1147/rd.14.0309 10.1007/978-3-540-77046-6_62 10.1111/j.1467-9450.1974.tb00598.x 10.1109/SITIS.2008.47 10.1016/j.ipm.2007.01.015 10.1016/j.bushor.2011.01.005 10.1002/9780470689646.ch1 10.3115/1119355.1119383 10.1002/aris.1440370103 10.1016/0378-8733(94)00248-9 10.3115/1613172.1613178 10.1002/asi.4630260106 10.1145/1244002.1244182 |
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| References | S. Rose, D. Engel, N. Cramer, W. Cowley, Automatic Keyword Extraction from Individual Documents, Text Mining: Applications and Theory, 2010. Han, Kamber (b0325) 2001 S. Asur, B.A. Huberman, Predicting the future with social media, in: 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 1, IEEE, 2010, pp. 492–499. M. Litvak, M. Last, Graph-based keyword extraction for single-document summarization, in: Proceedings of the Workshop on Multi-source Multilingual Information Extraction and Summarization, 2008, p. 17–24. Wasserman, Faust, Iacobucci (b0170) 1995 Chowdhury (b0080) 2003; 37 R. Feldman, J. Sanger, The Text Mining Handbook Advanced Approaches in Analysing Unstructured Data, [S.l.]: Cambridge, 2007. Hage, Harary (b0175) 1995; 17 Kaplan, Haenlein (b0010) 2010; 53 E. Frank, G.W. Paynter, I.H. Witten, Domain-specific keyphrase extraction, in: Proceedings of the 16th International Joint Conference on Artificial Intelligence, 1999. Safko (b0270) 2010 A. Hulth, Improved automatic keyword extraction given more linguistic knowledge, in: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, 2003, p. 216–223. Earle, Bowden, Guy (b0035) 2011; 54 C.A. Chahine, N. Chaignaud, J.Ph. Kotowicz, J.P. Pécuche, Context and keyword extraction in plain text using a graph representation, in: Proceedings of the 2008 IEEE International Conference on Signal Image Technology and Internet Based Systems, vol. 8, 2008, p. 692–696. C.D. Manning, Foundations of statistical natural language processing, in: H. Schütze (Ed.), MIT Press, 1999. Kietzmann, Hermkens, McCarthy, Silvestre (b0265) 2011; 54 Kowalski (b0250) 2011 Corley, Cook, Mikler, Singh (b0045) 2010; 7 Palshikar (b0330) 2007; 4815 Frakes, Baeza-Yates (b0285) 1992 (b0055) 2004 Salton, Yang, Yu (b0295) 1975 Cohen, Hersh (b0060) 2005; 6 Gupta, Lehal (b0070) 2009; 1 M. Yoshida, S. Matsushima, S. Ono, I. Sato, H. Nakagawa, ITC-UT: tweet categorization by query categorization of on-line reputation management, in: Conference on Multilingual and Multimodal Information Access Evaluation, 2010. M.A. Russell, Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More. O’Reilly Media Inc., 2013. Hirschman, Thompson (b0275) 1997; vol. XII–XIII Luhn (b0310) 1957 Baeza-Yates, Ribeiro-Neto (b0090) 1999 I.H. Witten, G.W. Paynter, E. Frank, C. Gutwin, C.G. Nevill-Manning, KEA practical automatic keyphrase action, in: Proceedings of the 4th ACM Conference on Digital Library (DL’99), Berkeley, CA, USA, 1999, p. 254–226. P.D. Turney, Learning to Extract Keyphrases from Text, NRC Technical Report ERB-1057, National Research Council, Canada, 1999, p. 1–43. Gross, Yellen (b0305) 2006 K. Zhang, H. Xu, J. Tang, J.Z. Li, Keyword extraction using support vector machine, in: Proceedings of the Seventh International Conference on Web-Age, Information Management (Waim2006), 2006. B. Lott, Survey of Keyword Extraction Techniques, UNM Education, 2012. Matsuo, Ishizuka (b0315) 2004; 4 Salton, McGill (b0280) 1983 Manning, Raghavan, Schütze (b0290) 2008; vol. 1 F. Zhou, F. Zhang, B. Yang, Graph-based text representation model and its realization, in: 2010 International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE), vol. 1, No. 8, 2010, p. 21–23. Datasift, Browse Data Sources – Twitter, 2012. [Online]. Available S. Hensman, Construction of conceptual graph representation of texts, in: Proceedings of Student Research Workshop at HLT-NAACL, Boston, 2004, p. 49–54. E. Agichtein, C. Castillo, D. Donato, A. Gionis, G. Mishne, Finding high-quality content in social media, in: Proceedings of the 2008 International Conference on Web Search and Data Mining, ACM, 2008, pp. 183–194. Nieminen (b0165) 1974; 15 W.X. Zhao, J. Jiang, J. He, Y. Song, P. Achananuparp, E.-P. Lim, X. Li, Topical keyphrase extraction from Twitter, in: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (HLT ‘11), Association for Computational Linguistics, vol. 1, Stroudsburg, PA, USA, 2011, p. 379–388. Erckan, Cicekli (b0320) 2007 Zhang, Wang, Liu, Wu, Liao, Wang (b0300) 2008 [Accessed24October 2013]. D. Ediger, K. Jiang, J. Riedy, D.A. Bader, C. Corley, R. Farber, W.N. Reynolds, Massive social network analysis: mining twitter for social good, in: 2010 39th International Conference on Parallel Processing (ICPP), IEEE, 2010, pp. 583–593. A. Schenker, M. Last, H. Bunke, Classification of web documents using a graph model, in: Proceedings of 7th International Conference on Document Analysis and Recognition (ICDAR2003), Computer Society Press, Scotland, 2003. A. Bermingham, A. Smeaton, On Using Twitter to Monitor Political Sentiment and Predict Election Results, Sentiment Analysis where AI meets Psychology, 2011, pp. 2–10. Y. Ohsawa, N.E. Benson, M. Yachida, KeyGraph: automatic indexing by co-occurrence graph based on building construction metaphor, in: Proceedings. IEEE International Forum on Research and Technology Advances in Digital Libraries, 1998, ADL 98, p. 12–18. Hotho, Nürnberger, Paaß (b0065) 2005; 20 W. Jin, R.K. Srihari, Graph-based text representation and knowledge discovery, in: Proceedings of the 2007 ACM Symposium on Applied, Computing, vol. 7, 2007, pp. 807–811. S.F. Dennis, The design and testing of a fully automatic indexing–searching system for documents consisting of expository text, in: G. Schecter (Eds.), Information Retrieval: A Critical Review, 1967. Earle (10.1016/j.amc.2014.04.090_b0035) 2011; 54 Wasserman (10.1016/j.amc.2014.04.090_b0170) 1995 Palshikar (10.1016/j.amc.2014.04.090_b0330) 2007; 4815 10.1016/j.amc.2014.04.090_b0015 10.1016/j.amc.2014.04.090_b0215 10.1016/j.amc.2014.04.090_b0050 Nieminen (10.1016/j.amc.2014.04.090_b0165) 1974; 15 10.1016/j.amc.2014.04.090_b0245 Kowalski (10.1016/j.amc.2014.04.090_b0250) 2011 10.1016/j.amc.2014.04.090_b0125 Matsuo (10.1016/j.amc.2014.04.090_b0315) 2004; 4 10.1016/j.amc.2014.04.090_b0205 10.1016/j.amc.2014.04.090_b0160 10.1016/j.amc.2014.04.090_b0085 10.1016/j.amc.2014.04.090_b0040 (10.1016/j.amc.2014.04.090_b0055) 2004 10.1016/j.amc.2014.04.090_b0120 10.1016/j.amc.2014.04.090_b0240 10.1016/j.amc.2014.04.090_b0190 Kaplan (10.1016/j.amc.2014.04.090_b0010) 2010; 53 Hirschman (10.1016/j.amc.2014.04.090_b0275) 1997; vol. XII–XIII Cohen (10.1016/j.amc.2014.04.090_b0060) 2005; 6 10.1016/j.amc.2014.04.090_b0115 Gross (10.1016/j.amc.2014.04.090_b0305) 2006 10.1016/j.amc.2014.04.090_b0235 Salton (10.1016/j.amc.2014.04.090_b0295) 1975 Baeza-Yates (10.1016/j.amc.2014.04.090_b0090) 1999 10.1016/j.amc.2014.04.090_b0030 Manning (10.1016/j.amc.2014.04.090_b0290) 2008; vol. 1 10.1016/j.amc.2014.04.090_b0150 10.1016/j.amc.2014.04.090_b0230 Chowdhury (10.1016/j.amc.2014.04.090_b0080) 2003; 37 10.1016/j.amc.2014.04.090_b0155 Erckan (10.1016/j.amc.2014.04.090_b0320) 2007 Luhn (10.1016/j.amc.2014.04.090_b0310) 1957 10.1016/j.amc.2014.04.090_b0180 Hage (10.1016/j.amc.2014.04.090_b0175) 1995; 17 Salton (10.1016/j.amc.2014.04.090_b0280) 1983 Zhang (10.1016/j.amc.2014.04.090_b0300) 2008 10.1016/j.amc.2014.04.090_b0225 Kietzmann (10.1016/j.amc.2014.04.090_b0265) 2011; 54 Gupta (10.1016/j.amc.2014.04.090_b0070) 2009; 1 Safko (10.1016/j.amc.2014.04.090_b0270) 2010 10.1016/j.amc.2014.04.090_b0140 10.1016/j.amc.2014.04.090_b0260 Hotho (10.1016/j.amc.2014.04.090_b0065) 2005; 20 Han (10.1016/j.amc.2014.04.090_b0325) 2001 10.1016/j.amc.2014.04.090_b0020 10.1016/j.amc.2014.04.090_b0185 Frakes (10.1016/j.amc.2014.04.090_b0285) 1992 Corley (10.1016/j.amc.2014.04.090_b0045) 2010; 7 10.1016/j.amc.2014.04.090_b0220 |
| References_xml | – volume: 7 start-page: 596 year: 2010 end-page: 615 ident: b0045 article-title: Text and structural data mining of influenza mentions in web and social media publication-title: Int. J. Environ. Res. Publ. Health – volume: 37 start-page: 51 year: 2003 end-page: 89 ident: b0080 article-title: Natural language processing publication-title: Annu. Rev. Inf. Sci. Technol. – start-page: 1169 year: 2008 end-page: 1180 ident: b0300 article-title: Automatic keyword extraction from documents using conditional random fields publication-title: J. Comput. Inf. Syst. – year: 2006 ident: b0305 article-title: Graph Theory and Its Applications – reference: I.H. Witten, G.W. Paynter, E. Frank, C. Gutwin, C.G. Nevill-Manning, KEA practical automatic keyphrase action, in: Proceedings of the 4th ACM Conference on Digital Library (DL’99), Berkeley, CA, USA, 1999, p. 254–226. – volume: 53 start-page: 59 year: 2010 end-page: 68 ident: b0010 article-title: Users of the world, unite! The challenges and opportunities of social media publication-title: Bus. Horizons – year: 2007 ident: b0320 article-title: Using lexical chains for keyword extraction publication-title: Inf. Processing Manage. – reference: E. Frank, G.W. Paynter, I.H. Witten, Domain-specific keyphrase extraction, in: Proceedings of the 16th International Joint Conference on Artificial Intelligence, 1999. – year: 2001 ident: b0325 article-title: Data Mining: Concepts and Techniques – volume: 4815 start-page: 503 year: 2007 end-page: 510 ident: b0330 article-title: Keyword extraction from a single document using centrality measures publication-title: Pattern Recogn. Mach. Intell. – reference: W. Jin, R.K. Srihari, Graph-based text representation and knowledge discovery, in: Proceedings of the 2007 ACM Symposium on Applied, Computing, vol. 7, 2007, pp. 807–811. – volume: 1 start-page: 60 year: 2009 end-page: 76 ident: b0070 article-title: A survey of text mining techniques and applications publication-title: J. Emerg. Technol. Web Intell. – year: 2010 ident: b0270 article-title: The Social Media Bible: Tactics, Tools, and Strategies for Business Success – volume: 15 start-page: 332 year: 1974 end-page: 336 ident: b0165 article-title: On the centrality in a graph publication-title: Scand. J. Psychol. – volume: 54 start-page: 241 year: 2011 end-page: 251 ident: b0265 article-title: Social media? Get serious! Understanding the functional building blocks of social media publication-title: Bus. Horizons – reference: M. Litvak, M. Last, Graph-based keyword extraction for single-document summarization, in: Proceedings of the Workshop on Multi-source Multilingual Information Extraction and Summarization, 2008, p. 17–24. – year: 1995 ident: b0170 article-title: Social Network Analysis: Methods and Applications – reference: >. [Accessed24October 2013]. – reference: A. Schenker, M. Last, H. Bunke, Classification of web documents using a graph model, in: Proceedings of 7th International Conference on Document Analysis and Recognition (ICDAR2003), Computer Society Press, Scotland, 2003. – volume: 20 start-page: 19 year: 2005 end-page: 62 ident: b0065 article-title: A brief survey of text mining publication-title: Ldv Forum – volume: 54 start-page: 708 year: 2011 end-page: 715 ident: b0035 article-title: Twitter earthquake detection: earthquake monitoring in a social world publication-title: Ann. Geophys. – reference: B. Lott, Survey of Keyword Extraction Techniques, UNM Education, 2012. – reference: A. Hulth, Improved automatic keyword extraction given more linguistic knowledge, in: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, 2003, p. 216–223. – reference: Datasift, Browse Data Sources – Twitter, 2012. [Online]. Available: < – volume: 17 start-page: 57 year: 1995 end-page: 63 ident: b0175 article-title: Eccentricity and centrality in networks publication-title: Soc. Networks – reference: S.F. Dennis, The design and testing of a fully automatic indexing–searching system for documents consisting of expository text, in: G. Schecter (Eds.), Information Retrieval: A Critical Review, 1967. – reference: P.D. Turney, Learning to Extract Keyphrases from Text, NRC Technical Report ERB-1057, National Research Council, Canada, 1999, p. 1–43. – volume: vol. 1 year: 2008 ident: b0290 publication-title: Introduction to Information Retrieval – reference: C.D. Manning, Foundations of statistical natural language processing, in: H. Schütze (Ed.), MIT Press, 1999. – reference: M. Yoshida, S. Matsushima, S. Ono, I. Sato, H. Nakagawa, ITC-UT: tweet categorization by query categorization of on-line reputation management, in: Conference on Multilingual and Multimodal Information Access Evaluation, 2010. – volume: 4 year: 2004 ident: b0315 article-title: Keyword extraction from a single document using word co-occurrence statistical information publication-title: Int. J. Artif. Intell. Tools – year: 1957 ident: b0310 article-title: A statistical approach to mechanized encoding and searching of literary information publication-title: IBM J. Res. Dev. – reference: M.A. Russell, Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More. O’Reilly Media Inc., 2013. – reference: S. Hensman, Construction of conceptual graph representation of texts, in: Proceedings of Student Research Workshop at HLT-NAACL, Boston, 2004, p. 49–54. – year: 2011 ident: b0250 article-title: Information Retrieval Architecture and Algorithms – volume: vol. XII–XIII start-page: 409 year: 1997 end-page: 414 ident: b0275 article-title: Overview of evaluation in speech and natural language processing publication-title: Survey of the State of the Art in Human Language Technology, Cambridge Studies in Natural Language Processing Series – year: 1983 ident: b0280 article-title: Introduction to Modern Information Retrieval – reference: A. Bermingham, A. Smeaton, On Using Twitter to Monitor Political Sentiment and Predict Election Results, Sentiment Analysis where AI meets Psychology, 2011, pp. 2–10. – reference: C.A. Chahine, N. Chaignaud, J.Ph. Kotowicz, J.P. Pécuche, Context and keyword extraction in plain text using a graph representation, in: Proceedings of the 2008 IEEE International Conference on Signal Image Technology and Internet Based Systems, vol. 8, 2008, p. 692–696. – reference: E. Agichtein, C. Castillo, D. Donato, A. Gionis, G. Mishne, Finding high-quality content in social media, in: Proceedings of the 2008 International Conference on Web Search and Data Mining, ACM, 2008, pp. 183–194. – reference: Y. Ohsawa, N.E. Benson, M. Yachida, KeyGraph: automatic indexing by co-occurrence graph based on building construction metaphor, in: Proceedings. IEEE International Forum on Research and Technology Advances in Digital Libraries, 1998, ADL 98, p. 12–18. – year: 1999 ident: b0090 article-title: Modern Information Retrieval – reference: R. Feldman, J. Sanger, The Text Mining Handbook Advanced Approaches in Analysing Unstructured Data, [S.l.]: Cambridge, 2007. – reference: F. Zhou, F. Zhang, B. Yang, Graph-based text representation model and its realization, in: 2010 International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE), vol. 1, No. 8, 2010, p. 21–23. – reference: K. Zhang, H. Xu, J. Tang, J.Z. Li, Keyword extraction using support vector machine, in: Proceedings of the Seventh International Conference on Web-Age, Information Management (Waim2006), 2006. – year: 1975 ident: b0295 article-title: A theory of term importance in automatic text analysis publication-title: J. Am. Soc. Inf. Sci. – reference: S. Asur, B.A. Huberman, Predicting the future with social media, in: 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 1, IEEE, 2010, pp. 492–499. – year: 2004 ident: b0055 publication-title: Survey of Text Mining – year: 1992 ident: b0285 article-title: Information Retrieval: Data Structures and Algorithms – volume: 6 start-page: 57 year: 2005 end-page: 71 ident: b0060 article-title: A survey of current work in biomedical text mining publication-title: Briefings Bioinf. – reference: D. Ediger, K. Jiang, J. Riedy, D.A. Bader, C. Corley, R. Farber, W.N. Reynolds, Massive social network analysis: mining twitter for social good, in: 2010 39th International Conference on Parallel Processing (ICPP), IEEE, 2010, pp. 583–593. – reference: S. Rose, D. Engel, N. Cramer, W. Cowley, Automatic Keyword Extraction from Individual Documents, Text Mining: Applications and Theory, 2010. – reference: W.X. Zhao, J. Jiang, J. He, Y. Song, P. Achananuparp, E.-P. Lim, X. Li, Topical keyphrase extraction from Twitter, in: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (HLT ‘11), Association for Computational Linguistics, vol. 1, Stroudsburg, PA, USA, 2011, p. 379–388. – ident: 10.1016/j.amc.2014.04.090_b0125 doi: 10.1109/ICPP.2010.66 – ident: 10.1016/j.amc.2014.04.090_b0160 doi: 10.3115/1614038.1614047 – ident: 10.1016/j.amc.2014.04.090_b0180 doi: 10.1007/11775300_8 – ident: 10.1016/j.amc.2014.04.090_b0225 – year: 1992 ident: 10.1016/j.amc.2014.04.090_b0285 – start-page: 1169 year: 2008 ident: 10.1016/j.amc.2014.04.090_b0300 article-title: Automatic keyword extraction from documents using conditional random fields publication-title: J. Comput. Inf. Syst. – ident: 10.1016/j.amc.2014.04.090_b0260 – ident: 10.1016/j.amc.2014.04.090_b0150 doi: 10.1109/NLPKE.2010.5587861 – year: 2010 ident: 10.1016/j.amc.2014.04.090_b0270 – year: 2001 ident: 10.1016/j.amc.2014.04.090_b0325 – year: 2011 ident: 10.1016/j.amc.2014.04.090_b0250 – year: 1999 ident: 10.1016/j.amc.2014.04.090_b0090 – volume: 53 start-page: 59 issue: 1 year: 2010 ident: 10.1016/j.amc.2014.04.090_b0010 article-title: Users of the world, unite! The challenges and opportunities of social media publication-title: Bus. Horizons doi: 10.1016/j.bushor.2009.09.003 – volume: 7 start-page: 596 issue: 2 year: 2010 ident: 10.1016/j.amc.2014.04.090_b0045 article-title: Text and structural data mining of influenza mentions in web and social media publication-title: Int. J. Environ. Res. Publ. Health doi: 10.3390/ijerph7020596 – year: 2004 ident: 10.1016/j.amc.2014.04.090_b0055 – ident: 10.1016/j.amc.2014.04.090_b0190 – ident: 10.1016/j.amc.2014.04.090_b0235 doi: 10.1109/ADL.1998.670375 – ident: 10.1016/j.amc.2014.04.090_b0215 – ident: 10.1016/j.amc.2014.04.090_b0015 doi: 10.1145/1341531.1341557 – ident: 10.1016/j.amc.2014.04.090_b0085 – volume: 6 start-page: 57 issue: 1 year: 2005 ident: 10.1016/j.amc.2014.04.090_b0060 article-title: A survey of current work in biomedical text mining publication-title: Briefings Bioinf. doi: 10.1093/bib/6.1.57 – ident: 10.1016/j.amc.2014.04.090_b0050 doi: 10.1017/CBO9780511546914 – ident: 10.1016/j.amc.2014.04.090_b0020 doi: 10.1109/WI-IAT.2010.63 – ident: 10.1016/j.amc.2014.04.090_b0155 – year: 1983 ident: 10.1016/j.amc.2014.04.090_b0280 – volume: vol. 1 year: 2008 ident: 10.1016/j.amc.2014.04.090_b0290 – ident: 10.1016/j.amc.2014.04.090_b0120 – year: 1957 ident: 10.1016/j.amc.2014.04.090_b0310 article-title: A statistical approach to mechanized encoding and searching of literary information publication-title: IBM J. Res. Dev. doi: 10.1147/rd.14.0309 – volume: 20 start-page: 19 issue: 1 year: 2005 ident: 10.1016/j.amc.2014.04.090_b0065 article-title: A brief survey of text mining publication-title: Ldv Forum – volume: 4815 start-page: 503 year: 2007 ident: 10.1016/j.amc.2014.04.090_b0330 article-title: Keyword extraction from a single document using centrality measures publication-title: Pattern Recogn. Mach. Intell. doi: 10.1007/978-3-540-77046-6_62 – ident: 10.1016/j.amc.2014.04.090_b0040 – year: 2006 ident: 10.1016/j.amc.2014.04.090_b0305 – volume: 54 start-page: 708 issue: 6 year: 2011 ident: 10.1016/j.amc.2014.04.090_b0035 article-title: Twitter earthquake detection: earthquake monitoring in a social world publication-title: Ann. Geophys. – volume: 15 start-page: 332 year: 1974 ident: 10.1016/j.amc.2014.04.090_b0165 article-title: On the centrality in a graph publication-title: Scand. J. Psychol. doi: 10.1111/j.1467-9450.1974.tb00598.x – ident: 10.1016/j.amc.2014.04.090_b0245 doi: 10.1109/SITIS.2008.47 – year: 2007 ident: 10.1016/j.amc.2014.04.090_b0320 article-title: Using lexical chains for keyword extraction publication-title: Inf. Processing Manage. doi: 10.1016/j.ipm.2007.01.015 – volume: 54 start-page: 241 issue: 3 year: 2011 ident: 10.1016/j.amc.2014.04.090_b0265 article-title: Social media? Get serious! Understanding the functional building blocks of social media publication-title: Bus. Horizons doi: 10.1016/j.bushor.2011.01.005 – ident: 10.1016/j.amc.2014.04.090_b0185 doi: 10.1002/9780470689646.ch1 – ident: 10.1016/j.amc.2014.04.090_b0205 doi: 10.3115/1119355.1119383 – ident: 10.1016/j.amc.2014.04.090_b0230 – volume: 1 start-page: 60 issue: 1 year: 2009 ident: 10.1016/j.amc.2014.04.090_b0070 article-title: A survey of text mining techniques and applications publication-title: J. Emerg. Technol. Web Intell. – volume: 37 start-page: 51 issue: 1 year: 2003 ident: 10.1016/j.amc.2014.04.090_b0080 article-title: Natural language processing publication-title: Annu. Rev. Inf. Sci. Technol. doi: 10.1002/aris.1440370103 – volume: 17 start-page: 57 year: 1995 ident: 10.1016/j.amc.2014.04.090_b0175 article-title: Eccentricity and centrality in networks publication-title: Soc. Networks doi: 10.1016/0378-8733(94)00248-9 – ident: 10.1016/j.amc.2014.04.090_b0240 doi: 10.3115/1613172.1613178 – year: 1975 ident: 10.1016/j.amc.2014.04.090_b0295 article-title: A theory of term importance in automatic text analysis publication-title: J. Am. Soc. Inf. Sci. doi: 10.1002/asi.4630260106 – volume: vol. XII–XIII start-page: 409 year: 1997 ident: 10.1016/j.amc.2014.04.090_b0275 article-title: Overview of evaluation in speech and natural language processing – ident: 10.1016/j.amc.2014.04.090_b0220 – ident: 10.1016/j.amc.2014.04.090_b0140 doi: 10.1145/1244002.1244182 – volume: 4 year: 2004 ident: 10.1016/j.amc.2014.04.090_b0315 article-title: Keyword extraction from a single document using word co-occurrence statistical information publication-title: Int. J. Artif. Intell. Tools – ident: 10.1016/j.amc.2014.04.090_b0115 – ident: 10.1016/j.amc.2014.04.090_b0030 – year: 1995 ident: 10.1016/j.amc.2014.04.090_b0170 |
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