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- Paradis, Carita, et al.
(författare)
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Negation and approximation as configurational construals in SPACE
- 2013
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Ingår i: The Construal of Spatial Meaning Windows into Conceptual Space. - 9780199641635
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Bokkapitel (refereegranskat)abstract
- This article is a window into conceptual space through antonyms, negated antonyms and antonyms modified by degree modifiers. It investigates native speakers’ understanding of negation in combination with BOUNDED antonymic adjectival meanings and also in relation to their interpretations of the approximating degree modifier, ‘almost’ in Swedish. The results of the investigation are compared with a similar study by Paradis and Willners (2006), which includes ‘not’ with UNBOUNDED SCALE meanings and in relation to ‘fairly’. We propose that ‘not’ is a degree modifier and like all other degree modifiers it operates on the configurational construals in SPACE. In combination with BOUNDED antonyms ‘not’ operates on the boundary and bisects a spatial structure. The combinations of ‘not’ and BOUNDED meanings are interpreted as synonyms of their antonyms. ‘Almost closed’ differs significantly from ‘closed’ but is not significantly different from ‘not open’. In contrast, in combination with UNBOUNDED antonyms, ‘not’ modifies the UNBOUNDED SCALE structure and evokes a range on the scale in SPACE in the same way as ‘fairly’ does. While the results for the UNBOUNDED meanings are very robust across all test items, the BOUNDED meanings are much more volatile and adaptive to alternative scalar interpretations.
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| 4. |
- Kucher, Kostiantyn, et al.
(författare)
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Visual Analysis of Stance Markers in Online Social Media
- 2014
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Ingår i: Poster Abstracts of IEEE VIS 2014. - : IEEE. ; , s. 259-260
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Konferensbidrag (refereegranskat)abstract
- Stance in human communication is a linguistic concept relating to expressions of subjectivity such as the speakers’ attitudes and emotions. Taking stance is crucial for the social construction of meaning and can be useful for many application fields such as business intelligence, security analytics, or social media monitoring. In order to process large amounts of text data for stance analyses, linguists need interactive tools to explore the textual sources as well as the results of computational linguistics techniques. Both aspects are important for refining the analyses iteratively. In this work, we present a visual analytics tool for online social media text data and corresponding time-series that can be used to investigate stance phenomena and to refine the so-called stance markers collection.
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| 6. |
- Kucher, Kostiantyn, et al.
(författare)
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Visual Analysis of Sentiment and Stance in Social Media Texts
- 2018
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Ingår i: EuroVis 2018 - Posters. - : Eurographics - European Association for Computer Graphics. - 9783038680659 ; , s. 49-51
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Konferensbidrag (refereegranskat)abstract
- Despite the growing interest for visualization of sentiments and emotions in textual data, the task of detecting and visualizing various stances is not addressed well by the existing approaches. The challenges associated with this task include development of the underlying computational methods and visualization of the corresponding multi-label stance classification results. In this poster abstract, we describe the ongoing work on a visual analytics platform called StanceVis Prime, which is designed for analysis of sentiment and stance in temporal text data from various social media data sources. Our approach consumes documents from several text stream sources, applies sentiment and stance classification, and provides end users with both an overview of the resulting data series and a detailed view for close reading and examination of the classifiers’ output. The intended use case scenarios for StanceVis Prime include social media monitoring and research in sociolinguistics.
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| 9. |
- Kucher, Kostiantyn, et al.
(författare)
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Visual Analysis of Online Social Media to Open Up the Investigation of Stance Phenomena
- 2016
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Ingår i: Information Visualization. - : Sage Publications. - 1473-8716 .- 1473-8724. ; 15:2, s. 93-116
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Tidskriftsartikel (refereegranskat)abstract
- Online social media are a perfect text source for stance analysis. Stance in human communication is concerned with speaker attitudes, beliefs, feelings and opinions. Expressions of stance are associated with the speakers' view of what they are talking about and what is up for discussion and negotiation in the intersubjective exchange. Taking stance is thus crucial for the social construction of meaning. Increased knowledge of stance can be useful for many application fields such as business intelligence, security analytics, or social media monitoring. In order to process large amounts of text data for stance analyses, linguists need interactive tools to explore the textual sources as well as the processed data based on computational linguistics techniques. Both original texts and derived data are important for refining the analyses iteratively. In this work, we present a visual analytics tool for online social media text data that can be used to open up the investigation of stance phenomena. Our approach complements traditional linguistic analysis techniques and is based on the analysis of utterances associated with two stance categories: sentiment and certainty. Our contributions include (1) the description of a novel web-based solution for analyzing the use and patterns of stance meanings and expressions in human communication over time; and (2) specialized techniques used for visualizing analysis provenance and corpus overview/navigation. We demonstrate our approach by means of text media on a highly controversial scandal with regard to expressions of anger and provide an expert review from linguists who have been using our tool.
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| 10. |
- Kucher, Kostiantyn, et al.
(författare)
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Active learning and visual analytics for stance classification with ALVA
- 2017
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Ingår i: ACM Transactions on Interactive Intelligent Systems. - New York, NY, USA : Association for Computing Machinery. - 2160-6455 .- 2160-6463. ; 7:3
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Tidskriftsartikel (refereegranskat)abstract
- The automatic detection and classification of stance (e.g., certainty or agreement) in text data using natural language processing and machine-learning methods creates an opportunity to gain insight into the speakers' attitudes toward their own and other people's utterances. However, identifying stance in text presents many challenges related to training data collection and classifier training. To facilitate the entire process of training a stance classifier, we propose a visual analytics approach, called ALVA, for text data annotation and visualization. ALVA's interplay with the stance classifier follows an active learning strategy to select suitable candidate utterances for manual annotaion. Our approach supports annotation process management and provides the annotators with a clean user interface for labeling utterances with multiple stance categories. ALVA also contains a visualization method to help analysts of the annotation and training process gain a better understanding of the categories used by the annotators. The visualization uses a novel visual representation, called CatCombos, which groups individual annotation items by the combination of stance categories. Additionally, our system makes a visualization of a vector space model available that is itself based on utterances. ALVA is already being used by our domain experts in linguistics and computational linguistics to improve the understanding of stance phenomena and to build a st ance classifier for applications such as social media monitoring.
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