oa Detecting contrast patterns in newspaper articles by combining discourse analysis and text mining

References

1. Baker, P
   (2006) Using Corpora in Discourse Analysis. London: Continuum.
   [Google Scholar]
2. Baker, P. , C. Gabrielatos , M. Khosravinik , M. Krzyzanowski , T. McEnery , and R. Wodak
   (2008) A useful methodological synergy? Combining critical discourse analysis and corpus linguistics to examine discourses of refugees and asylum seekers in the UK press. Discourse Society 19.3: 273–306. doi: 10.1177/0957926508088962
   https://doi.org/10.1177/0957926508088962 [Google Scholar]
3. Balahur, A. , and R. Steinberger
   (2009) Rethinking sentiment analysis in the news: From theory to practice and back. In Proceedings of the 1st Workshop on Opinion Mining and Sentiment Analysis , Satellite to CAEPIA2009.
   [Google Scholar]
4. Bell, A
   (1991) The Language of News Media. Oxford: Blackwell.
   [Google Scholar]
5. Cendrowska, J
   (1987) PRISM: An algorithm for inducing modular rules. International Journal of Man- Machine Studies 27.4: 349–370. doi: 10.1016/S0020‑7373(87)80003‑2
   https://doi.org/10.1016/S0020-7373(87)80003-2 [Google Scholar]
6. Cohen, W
   (1995) Fast effective rule induction. In Proceedings of the 12th International Conference on Machine Learning , p. 115–123.
   [Google Scholar]
7. Cohen, W. , and Y. Singer
   (1999) Context-sensitive learning methods for text categorization. ACM Transactions on Information Systems (TOIS) 17.2: 141–173. doi: 10.1145/306686.306688
   https://doi.org/10.1145/306686.306688 [Google Scholar]
8. Daelemans, W. , S. Bucholz , and J. Veenstra
   (1999) Memory-based shallow parsing. In Proceedings of the Computational Natural Language Learning Workshop (CoNLL-99). Demo: www.cnts.ua.ac.be/cgi-bin/jmeyhi/MBSP-instant-webdemo.cgi
   [Google Scholar]
9. EU EOM Kenya
   (2008) Kenya: Final Report. General Elections27December 2007 (3April 2008) Brussel: EU EOM Kenya, retrieved fromwww.eueom.eu/ [01/03/2010].
10. Fairclough, N
    (1995) Media Discourse. London: Arnold.
    [Google Scholar]
11. Fayyad, U. , G. Piatetsky-Shapiro , and P. Smyth
    (1996) The KDD process for extracting useful knowledge from volumes of data. Communication of the ACM 39. 11: 27–34. doi: 10.1145/240455.240464
    https://doi.org/10.1145/240455.240464 [Google Scholar]
12. Feldman, R. , and J. Sanger
    (2007) The Text Mining Handbook. Advanced Approaches in Analyzing Unstructured Data. New York: Cambridge University Press.
    [Google Scholar]
13. Fielding, N.G. , and R.M. Lee
    (1998) Computer Analysis of Qualitative Research. London: Sage.
    [Google Scholar]
14. Finn, A. , and N. Kushmerick
    (2006) Learning to classify documents according to genre. InJournal of the American Society for Information Science and Technology 57.11: 1506–1518. doi: 10.1002/asi.20427
    https://doi.org/10.1002/asi.20427 [Google Scholar]
15. Fortuna, B. , C. Galleguillos , and N. Cristianini
    (2009) Detecting the bias in media with statistical learning methods. In N. Ashok , Srivastava and M. Saham (eds.), Text Mining: Theory and Applications. London: Taylor and Francis Publisher.
    [Google Scholar]
16. Fortuna, B. , M. Grobelnik , and D. Mladenić
    (2006) System for semi-automatic ontology construction. In Proceedings of the Demo Session at European Semantic Web Conference ESWC (2006).
    [Google Scholar]
17. Fortuna, B. , M. Grobelnik , and D. Mladenić
    (2007) OntoGen: Semi-automatic ontology editor. In M.J. Smith , and G. Salvendy (eds.), Proceedings of Human Interface, Part II, HCI International 2007, LNCS 4558, Springer, p. 309–318.
    [Google Scholar]
18. Galtung, J. , and M.H. Ruge
    (1965) The structure of foreign news: The presentation of the Congo, Cuba and Cyprus crises in four Norwegian newspapers. Journal of Peace Research2.1: 64–91. doi: 10.1177/002234336500200104
    https://doi.org/10.1177/002234336500200104 [Google Scholar]
19. Gibbs, G.R
    (2004) Computer-assisted Qualitative Data Analysis (CAQDAS). In M.S. Lewis-Beck , A. Bryman , and T.F. Liao (eds.), The Sage Encyclopedia of Social Science Research Methods (1). Thousand Oaks: Sage, p. 87–89.
    [Google Scholar]
20. Greevy, E.P. , and A.F. Smeaton
    (2004) Text categorisation of racist texts using a support vector machine. In Proceedings of 7es Journées internationales d’Analyse statistique des Données Textuelles JADT (1) . Leuven: PUL, p. 533–544.
    [Google Scholar]
21. Harcup, T
    (2004) Journalism: Principles and Practice. London: Sage.
    [Google Scholar]
22. Harris, R.J
    (2004) A Cognitive Psychology of Mass Communication (4th ed.) Mahwah: Lawrence Erlbaum.
    [Google Scholar]
23. Kennedy, G
    (1998) An Introduction to Corpus Linguistics. London: Longman.
    [Google Scholar]
24. Koller, V. , and G. Mautner
    (2004) Computer applications in critical discourse analysis. In C. Coffin , A. Hewings , and K. O'Halloran (eds.), Applying English Grammar: Functional and Corpus Approaches. London: Arnold, p. 216–228.
    [Google Scholar]
25. Krishnamurty, R
    (1996) Ethnic, racial and tribal: The language of racism?In C.R. Caldas-Coulthard , and M. Coulthard (eds.), Texts and Practices: Readings in Critical Discourse Analysis. London/New York: Routledge, p. 129–149.
    [Google Scholar]
26. Lee, C. , J.M. Chan , Z. Pan , and C.Y.K. So
    (2000) National prisms of a global 'Media Event'. In J. Curran , and M. Gurevitch (eds.), Mass Media and Society (3rd ed.). London: Arnold., p. 295–309.
    [Google Scholar]
27. Lin, W.-H. , E. Xing , and A. Hauptmann
    (2008) A joint topic and perspective model for ideological discourse. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases , p. 17–32.
    [Google Scholar]
28. Lindlof, T.R. , and B.C. Taylor
    (2011) Qualitative Communication Research Methods (3rd ed.). Thousand Oaks: Sage.
    [Google Scholar]
29. Liu, S.-Z. , and H.-P. Hu
    (2007) Text classification using sentential frequent item sets. InJournal of Computer Science and Technology22.2. Beijing: Institute of Computing Technology, p. 334–337. doi: 10.1007/s11390‑007‑9041‑7
    https://doi.org/10.1007/s11390-007-9041-7 [Google Scholar]
30. Liu, B
    (2010) Sentiment Analysis: A Multi-Faceted Problem. IEEE Intelligent Systems25.3. doi: 10.1109/MIS.2010.86
    https://doi.org/10.1109/MIS.2010.86 [Google Scholar]
31. Lüdeling, A. , and M. Kytö
    (eds.) (2008) Corpus Linguistics. An International Handbook. Berlin: Mouton de Gruyter. doi: 10.1515/9783110211429
    https://doi.org/10.1515/9783110211429 [Google Scholar]
32. Luyckx, K
    (2010) Scalability Issues in Authorship Attribution. Brussels: UPA University Press Antwerp.
    [Google Scholar]
33. Luyckx, K. , and W. Daelemans
    (2008) Authorship attribution and verification with many authors and limited data. In Proceedings of the 22nd International Conference on Computational Linguistics (COLING 2008), p. 513–520.
    [Google Scholar]
34. Machin, D
    (2008) News discourse I: Understanding the social goings-on behind news texts. In A. Mayr (ed.), Language and Power: An Introduction to Institutional Discourse. London: Continuum, p. 62–89.
    [Google Scholar]
35. MacMillan, K
    (2005) More than just coding? Evaluating CAQDAS in a discourse analysis of news texts. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research6.3, art. 25.
    [Google Scholar]
36. Mahlberg, M
    (2007) Lexical items in discourse: Identifying local textual functions of sustainable development. In M. Hoey , M. Mahlberg , M. Stubbs , and W. Teubert (eds.), Text, Discourse and Corpora. Theory and Analysis. London/New York: Continuum, p. 191–218.
    [Google Scholar]
37. Matu, P.M. , and H.J. Lubbe
    (2007) Investigating language and ideology: A presentation of the ideological square and transitivity in the editorials of three Kenyan newspapers. Journal of Language and Politics 6.3: 401–418. doi: 10.1075/jlp.6.3.07mat
    https://doi.org/10.1075/jlp.6.3.07mat [Google Scholar]
38. Mautner, G
    (2007) Mining large corpora for social information: The case of elderly. Language in Society36.1: 51–72. doi: 10.1017/S0047404507070030
    https://doi.org/10.1017/S0047404507070030 [Google Scholar]
39. McGee, M.C
    (1980) The ‘ideograph’: A link between rhetoric and ideology. The Quarterly Journal of Speech66.1: 1–16. doi: 10.1080/00335638009383499
    https://doi.org/10.1080/00335638009383499 [Google Scholar]
40. Mitchell, T
    (1997) Machine Learning. Boston: McGraw Hill.
    [Google Scholar]
41. Morley, J. , and P. Bayley
    (2009) Corpus-Assisted Discourse Studies on the Iraq Conflict: Wording the War. New York: Routledge.
    [Google Scholar]
42. Ngonyani, D
    (2000) Tools of deception: Media coverage of student protests in Tanzania. Nordic Journal of African Studies9.2: 22–48.
    [Google Scholar]
43. Ogola, G
    (2009) Media at cross-roads: Reflections on the Kenyan news media and the coverage of the 2007 political crisis. Africa Insight39.1: 58–71.
    [Google Scholar]
44. O’Halloran, K
    (2010) How to use corpus linguistics in the study of media discourse. In A. O’Keeffe , and M. McCarthy (eds.), The Routledge Handbook of Corpus Linguistics. London/New York: Routledge, p.563–577.
    [Google Scholar]
45. O'Halloran, K. , and C. Coffin
    (2004) Checking overinterpretation and underinterpretation: Help from corpora in critical linguistics. In C. Coffin , A. Hewings , and K. O'Halloran (eds.), Applying English Grammar: Functional and Corpus Approaches. London: Arnold, p. 275–297.
    [Google Scholar]
46. O’Keeffe, A. , B. Clancy , and S. Adolphs
    (2011) Introducing Pragmatics in Use. London: Routledge.
    [Google Scholar]
47. Oloo, A.G.R
    (2007) The contemporary opposition in Kenya: Between internal traits and state manipulation. In G.R. Murunga , and S.W. Nasong’o (eds.), Kenya: The Struggle for Democracy. Dakar: CODESRIA Books, p. 90–125.
    [Google Scholar]
48. Pape, S. , and S. Featherstone
    (2005) Newspaper Journalism: A Practical Introduction. London: Sage.
    [Google Scholar]
49. Quinlan, J
    (1993) C4.5: Programs for Machine Learning. San Francisco: Morgan Kaufmann.
    [Google Scholar]
50. Rambaud, B
    (2008) Caught between information and condemnation: The Kenyan media in the electoral campaigns of December 2007. In J. Lafargue (ed.), The General Elections in Kenya, 2007 (Special issue of Les Cahiers d’Afrique de l’Est (38)). Nairobi: IFRA, p. 57–107.
    [Google Scholar]
51. Ray, C
    (2008) How the word 'tribe' stereotypes Africa. New African471: 8–9.
    [Google Scholar]
52. Reah, D
    (1998) The Language of Newspapers. London/New York: Routledge.
    [Google Scholar]
53. Richardson, J.E
    (2007) Analysing Newspapers: An Approach from Critical Discourse Analysis. Basingstoke: Palgrave Macmillan.
    [Google Scholar]
54. Rühlemann, C
    (2010) What can a corpus tell us about pragmatics?In A. O’Keeffe , and M. McCarthy (eds.), The Routledge Handbook of Corpus Linguistics. London/New York: Routledge, p.288–301.
    [Google Scholar]
55. Scott, M
    (2008) WordSmith Tools version 5, Liverpool: Lexical Analysis Software.
    [Google Scholar]
56. Schönfelder, W
    (2011) CAQDAS and qualitative syllogism logic—NVivo 8 and MAXQDA 10 Compared [91 paragraphs]. Forum Qualitative Sozialforschung/Forum: Qualitative Social Research12(1), art. 21.
    [Google Scholar]
57. Sebastiani, F
    (2002) Machine learning in automated text categorization. ACM Computing Surveys 34.1: 1–47. doi: 10.1145/505282.505283
    https://doi.org/10.1145/505282.505283 [Google Scholar]
58. Sinclair, J
    (1991) Corpus, Concordance, Collocation. Oxford: Oxford University Press.
    [Google Scholar]
59. Stamatatos, E. , N. Fakotakis , and G. Kokkinakis
    (2000) Automatic text categorization in terms of genre and author. Computational Linguistics 26.4: 471–495. doi: 10.1162/089120100750105920
    https://doi.org/10.1162/089120100750105920 [Google Scholar]
60. Stubbs, M
    (1996) Text and Corpus Analysis: Computer-assisted Studies of Language and Culture. Oxford: Blackwell.
    [Google Scholar]
61. Stubbs, M
    (2001) Texts, corpora, and problems of interpretation: A response to Widdowson. Applied Linguistics22.2: 149–172. doi: 10.1093/applin/22.2.149
    https://doi.org/10.1093/applin/22.2.149 [Google Scholar]
62. Thornbury, S
    (2010) What can a corpus tell us about discourse?In A. O’Keeffe , and M. McCarthy (eds.), The Routledge Handbook of Corpus Linguistics. London/New York: Routledge, p.270–287.
    [Google Scholar]
63. Van Dijk, T.A
    (1988) News as Discourse. Hillsdale: Lawrence Erlbaum.
    [Google Scholar]
64. Van Dijk, T.A
    (2006) Ideology and discourse analysis. Journal of Political Ideologies 11.2: 115–140. doi: 10.1080/13569310600687908
    https://doi.org/10.1080/13569310600687908 [Google Scholar]
65. Van Ginneken, J
    (2002) De schepping van de wereld in het nieuws: De 101 vertekeningen die elk 1 procent verschil maken (2nd ed.). Kluwer: Alphen aan den Rijn.
    [Google Scholar]
66. Van Leeuwen, T
    (2008) Discourse and Practice: New Tools for Critical Discourse Analysis. Oxford: Oxford University Press.
    [Google Scholar]
67. Verschueren, J
    (1996) Contrastive ideology research: Aspects of a pragmatic methodology. Language Sciences18.3/4: 589–603. doi: 10.1016/S0388‑0001(96)00036‑8
    https://doi.org/10.1016/S0388-0001(96)00036-8 [Google Scholar]
68. Verschueren, J
    (1999) Understanding Pragmatics. London: Arnold.
    [Google Scholar]
69. Verschueren, J
    (2008) Context and structure in a theory of pragmatics. Studies of Pragmatics 10: 13–23.
    [Google Scholar]
70. Westerståhl, J. , and F. Johansson
    (1994) Foreign news: News values and ideologies. European Journal of Communication9: 71–89. doi: 10.1177/0267323194009001004
    https://doi.org/10.1177/0267323194009001004 [Google Scholar]
71. Witten, I.H. , and E. Frank
    (2005) Data Mining Practical Machine Learning Tools and Techniques (2nd ed.). San Francisco: Elsevier.
    [Google Scholar]
72. Wrong, M
    (2008) Don’t mention the war. New Statesman137.4884: 22–23.
    [Google Scholar]
73. Wu, D.H
    (2007) A brave new world for international news? Exploring the determinants of the coverage of foreign nations on US websites. The International Communication Gazette69.6: 539–551. doi: 10.1177/1748048507082841
    https://doi.org/10.1177/1748048507082841 [Google Scholar]
74. Zhao, Y. , and J. Zobel
    (2005) Effective and scalable authorship attribution using function words, LNCS 3689, p. 174–189. Berlin/Heidelberg: Springer.
    [Google Scholar]