A Corpus-Based Study of Attitudes towards the Incident of the Thai Cave Boys
Article Sidebar
Main Article Content
Abstract
The recent incident regarding the Thai boys trapped in a cave is one that is well known. Not only the incident itself made it well-known but also the successful outcome due to cooperation of people worldwide. The purpose of this study was to analyse the attitudes of Twitter users through the language used in their comments. The corpus containing 16,621 tokens was compiled by collecting the language used in the comments on news of the Thai cave boys from the BBC and CNN official accounts on Twitter.com and analysed using word frequency, keyword analysis, dispersion, Linguistic Inquiry and Word Count (LIWC), and Semantic Tagger. The results showed that the matter that the Twitter users mentioned most was “the boys”. Furthermore, when considering the dispersion plots, the words “boys” occurred most across the corpus. This showed that “the boys” was the most interesting issue and also most concerned by Twitter users. The correlation results of LIWC and Semantic Tagger showed that the users had positive attitudes towards this incident. Whatever happens, people from different parts of the world still support each other.
Article Details
References
Allport, G. (1935). Attitudes. In C. Murchison (Ed.), A Handbook of Social Psychology (Vol. 2, pp. 798–844). Worcester, MA: Clark University Press.
Anthony, L. (2014). AntWordProfiler Version 1.4.1 (Software). Tokyo, Japan: Waseda University.
Anthony, L. (2018). AntConc Version 3.5.7 (Software). Tokyo, Japan: Waseda University.
Antonak, R., & Livneh, H. (1995). Direct and Indirect Methods to Measure Attitudes toward Persons with Disabilities, with an Exegesis of the Error-Choice Test Method. Rehabilitation Psychology, 40(1). 3-24.
Baker, C. (2006). Introduction to Language Policy: Theory and Method. In T. Ricento (Ed.), An Introduction to Language Policy: Theory and Method. (pp. 210–228). Oxford, UK: Blackwell.
Clement, J. (2019). Twitter: Number of Monthly Active Users 2010-2019. Retrieved from https://www.statista.com/statistics/282087/number-of-monthly-active-twitter-users/
Coxhead, A. (2000). A New Academic Word List. TESOL Quarterly, 34(2). 213-238.
Drasovean, A., & Tagg, C. (2015). Evaluative Language and its Solidarity-Building Role on TED.com: An Appraisal and Corpus Analysis. Language@Internet, 12(1).
Eagly, A., & Chaiken, S. (1993). The Psychology of Attitudes. New York: Harcourt Brace Jovanovich.
Gardner, R. (2002). Social Psychological Perspectives on Second Language Acquisition. In R. Kaplan (Ed.), The Oxford Handbook of Applied Linguistics (pp. 160–169). New York: Oxford University Press.
Garrett, P. (2010). Attitudes to Language. Cambridge, UK: Cambridge University Press.
Graham, D. (2014). Key-BNC (Software). Retrieved from http://crs2.kmutt.ac.th/Key-BNC/
Gries, S. (2010). Useful Corpus-linguistics Statistics. In A. Sánchez & M. Almela (Eds.), A Mosaic of Corpus Linguistics: Selected Approaches (pp. 269–291). Frankfurt am Main: Peter Lang.
Herring, S. (2008). Virtual Community. In L. M. Given (Ed.), Encyclopedia of Qualitative Research Methods (pp. 920-921). London: Sage.
Herring, S., Stein, D., & Virtanen, T. (2013). Pragmatics of Computer-Mediated Communication. Berlin, Germany: De Gruyter Mouton.
Ivković, D. (2013). The Eurovision Song Contest on YouTube: A Corpus-based Analysis of Language Attitudes. Language@Internet, 10(1).
Kennedy, G. (1998). An Introduction to Corpus Linguistics. London, New York: Longman.
Koppel, M., & Schler, J. (2006). The Importance of Neutral Examples for Learning Sentiment. Computational Intelligence, 22(2), pp. 100–109.
Lindquist, H. (2009). Corpus Linguistics and the Description of English. Edinburgh: Edinburgh University Press.
Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment Classification using Machine Learning Techniques. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP). pp. 79–86.
Pang, B., & Lee, L. (2004). A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts. In Proceedings of the Association for Computational Linguistics (ACL). pp. 271–278.
Pang, B., & Lee, L. (2005). Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the Association for Computational Linguistics (ACL). pp. 115–124.
Pennebaker, J., Booth, R., & Francis, M. (2007). Linguistic Inquiry and Word Count: LIWC [Computer software]. Austin, TX: LIWC.net.
Rayson, P., Archer, D., Piao, S., & McEnery, T. (2004). The UCREL semantic analysis system. In proceedings of the workshop on Beyond Named Entity Recognition Semantic labelling for NLP tasks in association with 4th International Conference on Language Resources and Evaluation (LREC 2004), 25th May 2004, Lisbon, Portugal, pp. 7-12.
Ryan, E., Giles, H., & Hewstone, M. (1988). The Measurement of Language Attitudes. In U. Ammon, N. Dittmar, & K. Mattheier (Eds.), Sociolinguistics: An International Handbook of the Science of Language and Society (pp. 1068–1082). Berlin: Walter de Gruyter.
Stubbs, M. (1996). Text and corpus analysis. Oxford: Blackwell.
Tausczik, Y., & Pennebaker, W. (2010). The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods. Journal of Language and Social Psychology, 29(1), 24-54.
Tsou, A., Thelwall, M. Mongeon, P., & Sugimoto, C. (2014). A Community of Curious Souls: An Analysis of Commenting Behavior on TED Talks Videos. PloS ONE, 9(4).
West, M. (1953). A General Service List of English Words. London: Longman.