Sociolinguistic Variationist Analysis of Word-Emotion Lexicon in Cook Islands English Online News
Abstract
This paper describes how journalists, in the Cook Islands, use sentiment lexicon when reporting online news. To do so, we employ Sentiment Analysis (SA) in combination with sociolinguistic variationist theory and logistic regression analysis. SA relies on the Word-Emotion Association Lexicon source (Mohammad & Turney 2013), which comprises 10,170 lexical items. The bulk of research carried out on sentiment analysis only distinguishes between positive vs. negative emotions. By contrast, we provide a fine-grained coding by exploring how eight specific core emotions (i.e. ANGER, ANTICIPATION, FEAR, DISGUST, JOY, SADNESS, SURPRISE, and TRUST) are socially stratified in formal contexts. We built a small-scale corpus from web-based newspapers to find out (i) whether social factors (age and sex) condition the use of sentiment lexicon and (ii) to evaluate the socially acknowledged generalisations according to which females tend to use sentiment lexicon more than males. The data was quantitatively examined through mixed-effects Rbrul logistic regression analysis. The independent variables include: word class (i.e. nous, adjectives, verbs), sex, age, and word-frequency. Specifically, the latter is a variable involved in language processing and is commonly studied in psycholinguistics, sociolinguistics, and corpus linguistics (Mickiewicz 2019). To account for word-frequency we use the SUBTLEX-US corpus (Brysbaert & New 2009). Our findings suggest that sentiment lexicon is conditioned by age, with young and old speakers favouring the use of sentiment lexicon. Sex, word class, and word-frequency do not have a significant influence on sentiment lexicon in our data.
References
Aikhenvald A. 2004. Evidentiality. Oxford: Oxford University Press.
Aldrich, N. J., Tenenbaum, H. R. 2006. Sadness, Anger, and Frustration: Gendered Patterns in Early Adolescents' and their Parents' Emotion Talk. “Sex Roles: A Journal of Research”, 55(11-12), pp. 775–785.
Amos, J. Kasstan, J. and Johnson, W. 2020. Reconsidering the variable context: A phonological argument for (t) and (d) deletion. “English Today”, 36(3), 6-13.
Batson CD, Shaw LL, Oleson KC. 1992. Differentiating affect, mood, and emotion: Toward functionally based conceptual distinctions. In Emotion. Review of Personality and Social Psychology, ed. MS Clark, Newbury Park, CA: Sage. pp. 294-326.
Bell, A. and Gibson, A. 2008. Stopping and Fronting in New Zealand Pasifika English, “University of Pennsylvania Working Papers in Linguistics”: Vol. 14.
Benamara, F., Cesarano, C., Picariello, A., Reforgiato, D., Venkatramana, R. and Subrahmanian, S. 2007. Sentiment analysis: Adjectives and adverbs are better than adjectives alone. “ICWSM”. Citeseer, 2007.
Benamara, F., Popescu V., Chardon, B., Asher, N. and Mathieu, Y. 2013. Assessing opinions in texts: Does discourse really matter? In Nonveridicality and Evaluation: Theoretical, Computational and Corpus Approaches, ed. M Taboada, R Trnavac, Leiden: Brill. pp. 127-50.
Biewer, C. 2015. South Pacific Englishes: A Sociolinguistic and Morphosyntactic Profile of Fiji English, Samoan English and Cook Islands English (Varieties of English around the World G52). Amsterdam, The Netherlands/Philadelphia, Pennsylvania: John Benjamins Publishing. pp. xvi + 341
Biber, D. and Finegan E. 1988. Adverbial stance types in English. “Discourse Processes” 11: 1-34.
Boucher, J. and Osgood, C. E. 1969. The pollyanna hypothesis. J. Verb. Learn. Verb. “Behav”. 8: 1–8.
Brysbaert, M. and New, B. 2009. Moving beyond Kucera and Francis: A critical evaluation of current word frequency norms and the introduction of a new and improved word frequency measure for American English. “Behavior Research Methods”, 41, 977-990.
Ciancia, C. & Gallo, A. 2021. Linguistic Fieldwork amid the Covid-19 Pandemic: The Impact of Social-distancing on Data Collection. “I-LanD Journal” (2), 135-153.
Colombetti, G. 2009. What language does to feelings. “Journal of Consciousness Studies”, 16(9), 4–26.
El-Beltagy SR and Ali A. 2013. Open issues in the sentiment analysis of Arabic social media: A case study. In “Proceedings of 9th International Conference on Innovations in Information Technology”. Al Ain, UAE.
Foolen, A. 2012. The relevance of emotions for language and linguistics. In A. Foolen et al. (eds) Moving Ourselves, Moving Others. Motion and Emotion in Intersubjectivity, Consciousness and Language. Consciousness & Emotion Book Series 6, 349–368. Amsterdam: John Benjamins.
Ghorbel H. 2012. Experiments in Cross-Lingual Sentiment Analysis in Discussion Forums. In Proceedings of the 4th International Conference on Social Informatics, ed. K Aberer, A Flache, W Jager, L Liu, J Tang, C Guéret, pp. 138-51. Berlin: Springer.
Goldschmidt, O. T. and Weller, L. 2000. Talking emotions: Gender differences in a variety of conversational contexts. “Symbolic Interaction”, 23, 117–134
Grossman, M. and Wood, W. 1993. Sex differences in intensity of emotional experience: A social role interpretation. “Journal of Personality and Social Psychology”, 65(5), 1010–1022.
Guy, G. R. and Torres Cacoullos, R. 2018. Reporting statistical results for LVC. Paper presented at “New Ways of Analyzing Variation (NWAV) 47”, 19 October, New York.
Guzzo, S. (Forthcoming) Cook Islands English: a newly-emerging variety of English?. Cambridge: CUP.
Haas M., and Versley Y. 2015. Subsentential sentiment on a shoestring: A crosslingual analysis of compositional classification. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics. Denver, CO.
Huang T-H., Yu H-C., and Chen H-H. 2012. Modeling Polyanna phenomena in Chinese sentiment analysis. In Proceedings of COLING 2012: Demonstration papers¡. 231-38.
Horne, L. 1989. A Natural History of Negation. The University of Chicago Pres, Chicago.
Holmes J and Meyerhoff M. Different Voices, Different Views: An Introduction to Current Research in Language and Gender. In (ed) The Handbook of Language and Gender. 1-17.
Johnson-Laird, P. N., and Oatley, K. 1989. The language of emotions: An analysis of a semantic field. “Cognition and Emotion”, 3(2), 81–123.
Kachru, B.B. 1985. Standards, codification and sociolinguistic realism: the English language in the outer circle. In R. Quirk and H.G. Widdowson (Eds) English in the World: teaching and Learning the Language and Literatures. Cambridge: Cambridge University Press. pp. 11-30.
Kachru, Braj B. 1992. Teaching World Englishes. In Braj B. Kachru (ed.), The other tongue: English across cultures, 2nd edn., 355–365.
Kachru, B-, B., Yamuna Kachru and Cecil L. N. 2006. (eds.), The handbook of World Englishes (Blackwell Handbooks in Linguistics). Oxford: Blackwell.
Kachru Y. and Nelson C. L. 2006. World Englishes in Asian Contexts. Hong Kong University Press.
Krippendorf K. 2004. Content Analysis: An Introduction to Its Methodology. Thousand Oaks, CA: Sage.
Lindquist, K.A. and Barrett, L.F. 2008. Emotional complexity. In M. Lewis, J. M. Haviland-Jones & L.F. (Eds.). Handbook of Emotions (3 ed.). NewYork: Guolford.
López, R., Tejada J. and Thelwall M. 2012. Spanish SentiStrength as a tool for opinion mining Peruvian Facebook and Twitter. In Artificial Intelligence Driven Solutions to Business and Engineering Problems, ed. G Setlak, M Alexandrov, K Markov, pp. 82-85. Sofia: Ithea.
Marchand M. 2012. État de l'art: l'influence du domaine sur la classification de l'opinion. In Proceedings of the joint conference JEP-TALN-RECITAL 2012, pp. 177-90. Grenoble, France.
Mohammad, S. and Yang, T. 2011. Tracking Sentiment in Mail: How Genders Differ on Emotional Axes. In Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA 2.011), 70–79, Portland, Oregon.
Mohammad, S. M. and Turney, P. D. 2013. Crowd sourcing a word–emotion association lexicon. “Computational Intelligence”, 29(3), 436–465.
Molina-González MD, Martínez-Cámara E, Martín-Valdivia M-T, Perea-Ortega JM. 2013. Semantic orientation for polarity classification in Spanish reviews. “Expert Systems with Applications” 40: 7250-57.
Moreno-Ortiz A. and Pérez Hernández L. 2012. Lexicon-based sentiment analysis of twitter messages in Spanish. In TASS, Taller de Análisis de Sentimientos en la SEPLN (Sociedad Española para el Procesamiento del Lenguaje Natural). Castellón de la Plana, Spain.
O'Connor, B., Balasubramanyan, R., Routledge, Bryan R. and Smith, Noah A., From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series. 2010. Tepper School of Business. Paper 559.
Omondi, L. 1997. The Language of Emotions. Amsterdam: Benjamins
Pak, A. and Paroubek, P. 2010. Twitter as a corpus for sentiment analysis and opinion mining. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10), pp 1320–1326.
Pavlenko, A. 2008. Emotion and emotion-laden words in the bilingual lexicon. “Bilingualism: Language and Cognition”, 11(2),
Portner P. 2009. Modality. Oxford: Oxford University Press.
Salameh M, Mohammad and S, Kiritchenko S. 2015. Sentiment analysis after translation: A case-study on Arabic social media posts. In Proceedings of the North American Chapter of the Association for Computational Linguistics (NAACL-2015). Denver, CO.
Sankoff, D. 1988. Sociolinguistcs and syntactic variation. In F. Newmeyer (ed.), Linguistics: The Cambridge Survey (pp. 140-161). Cambridge: Cambridge University Press.
Schweinberger, M. A. 2019. Sociolinguistic Analysis of Emotives. “Corpus Pragmatics” 3, 327–361.
Shimanoff, S. B. 1984. Commonly named emotions in everyday conversations. “Perceptual and Motor Skills”, 58(2), 514.
Stapley, J. C and Haviland, J. M. 1989. Beyond depression: Gender differences in normal adolescents' emotional experiences. “Sex Roles: A Journal of Research”, 20(5-6), 295–308.
Taboada M., Brooke J., Tofiloski M., Voll K., and Stede M. 2011. Lexicon-based methods for sentiment analysis. “Computational Linguistics” 37: 267-307.
Tagliamonte, S. 2006. Analysing Sociolinguistic Variation. Cambridge: Cambridge University Press.
Traugott EC. 2010. (Inter)subjectivity and (inter)subjectification: A reassessment. In Subjectification, Intersubjectification and Grammaticalization, ed. K Davidse, L Vandelanotte, H Cuyckens, pp. 29-74. Berlin: De Gruyter Mouton.
Valitutti, A., Strapparava, C., Stock, O. 2004. Developing affective lexical resources. “PsychNology Journal”, 2(1), 61 – 83.
Vilares, D, Alonso M., and Gòmez-Rodrìguez, C. 2015. A syntactic approach for opinion mining on Spanish reviews. “Natural Language Engineering”, 21(1), 139-163.
Waltinger, U. 2010. GermanPolarityClues: A Lexical Resource for German Sentiment Analysis. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), Valletta, Malta. European Language Resources Association (ELRA).
Wan X. 2008. Using bilingual knowledge and ensemble techniques for unsupervised Chinese sentiment analysis. In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, pp. 553-61. Honolulu.
Wang F., Wu Y. and Qiu L. 2012. Exploiting discourse relations for sentiment analysis. In Proceedings of the 24th International Conference on Computational Linguistics (COLING), Posters, pp. 1311-20. Mumbai.
Wang S., Jiang M., Duchesne Xavier M., Laugeson Elizabeth A., Kennedy Daniel P., Adolphs R. and Zhao Q. 2015. Atypical Visual Saliency in Autism Spectrum Disorder Quantified through Model-Based Eye Tracking. “Neuron”. 88:604–616.
Wiebe J., Wilson T., Bruce R., Bell M. and Martin M. 2004.Learning subjective language. “Computational Linguistics” 30: 277-308.
Wierzbicka, A. 1999. Emotions across languages and cultures. Diversity and universals. Cambridge: Cambridge University Press.
Winter, B. 2020. Statistics for Linguistics. An Introduction using R. New York: Routledge.
Weller, K., Bruns. A., Burgess, J., Merja Mahrt, and Puschmann, C. 2014. Twitter and Society. “The Journal of Media Innovation”, 134-137.
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