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A review of the use of portable technologies as observational aids in the classroom
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Joint modeling of users, questions and answers for answer selection in CQA
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Changes in science attitudes, beliefs, knowledge and physiological arousal after implementation of a multimodal, cooperative intervention in primary school science classes
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A sociolinguistic analysis of emotives
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Abstract:
This study details a replicable method for annotating emotionality of natural language that can be used in sociopragmatic, corpus-based analyses of discourse. A case study uses a type of sentiment analysis based on the crowd-sourced Word-Emotion Association Lexicon to investigate the social stratification of emotives, i.e. words associated with one of eight core emotions (ANGER, ANTICIPATION, FEAR, DISGUST, JOY, SADNESS; SURPRISE, and TRUST). The sentiment analysis is applied to dialogue data taken from the Irish component of the International Corpus of English and emotion scores provided by the sentiment analysis are correlated with the age and gender of speakers, the audience size, conversation type (same- vs. mixed gender conversation), dialogue setting (private vs. public), and part-of speech. The results of mixed-effects binomial regression models show that speakers use FEAR emotives significantly more frequently in public settings while JOY, DISGUST, and SURPRISE emotives are used more in private settings. In addition, men are significantly more likely to use ANGER and FEAR emotives, while women show higher rates of JOY emotives. Speakers aged 33 and older are more likely to use TRUST emotives compared with younger speakers. The results challenge common gendered social stereotypes according to which emotional language is associated with young women in particular. In contrast, the study shows that the genders exhibit emotion-specific preferences. In addition, the finding that negative emotions are more frequent in public discourse may indicate a general tendency even in apolitical conversation. However, the socio-political context in which the data were gathered has to be taken into account. It is highly likely that the linguistic expression of emotion was substantially affected by the communal tensions during the Northern Ireland conflict and findings should thus be treated with care not be naively generalized.
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Keyword:
1203 Language and Linguistics; 1706 Computer Science Applications; 3310 Linguistics and Language; Emotion language; Emotives; Gender differences; Sentiment analysis; Sociolinguistics
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URL: https://espace.library.uq.edu.au/view/UQ:87d92ef
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Characterising postgraduate students’ corpus query and usage patterns for disciplinary data-driven learning
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Learning factorized representations for open-set domain adaptation
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Multilevel topic dependency models for assessment design and delivery: A hypergraph based approach
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Detecting and visualizing context and stress via a fuzzy rule-based system during commuter driving
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Discovering correlations between sparse features in distant supervision for relation extraction
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Engagement and performance in a first year natural resource science course
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Deep context of citations using machine-learning models in scholarly full-text articles
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Using an online social media space to engage parents in student learning in the early-years: enablers and impediments
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Willis, Linda-Dianne; Exley, Beryl. - : Universitat de Barcelona * Grup de Recerca Ensenyament i Aprenentatge Virtual, Observatorio de la Educacion Digital (OED), 2018
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Measuring communication difficulty through effortful speech production during conversation
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An investigation of Chinese postgraduate students' experiences on a data-visualized English writing feedback platform
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Dai, Kun. - : Institute of Electrical and Electronics Engineers, 2017
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Social moments: A perspective on interaction for social robotics
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Retesting the limits of data-driven learning: feedback and error correction
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Searching for My Lady’s Bonnet: discovering poetry in the National Library of Australia’s newspapers database
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An investigation of differences and changes in L2 writing anxiety between blended and conventional english language learning context
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