Visualizing patterns of appraisal in texts and corpora

Almutairi, Bandar Alhumaidi A.

doi:10.1515/text-2013-0031

Cited by 9 publications

(9 citation statements)

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“…Most techniques that focus on polarity values use green for positive and red for negative polarity [WS08, TPTV16], although several techniques reverse this colour map to use green/blue as a cold hue corresponding to negative polarity, and orange/red for positive polarity [WFL*12, WFL*13, CAHF14, CFKA14, HC14, HC16, YWC*16]. The techniques related to emotions or affect categories other than positive/neutral/negative use either an ordinal set of colours representing categories [KR11 ZGWZ14, EAGA*16] or interpolate the colours based on the corresponding dimensional model [Alm13, WSK*15].…”

Section: Sentiment Visualization Techniquesmentioning

confidence: 99%

“…Kim and Lee [KL14] use several marker shapes as well as colour coding to differentiate between labeled/unlabelled positive and negative reviews in their scatterplot representation. Other techniques use the contour of a heat map or similar representation to convey emotion values [LR09, Alm13]. Kempter at al.…”

Section: Sentiment Visualization Techniquesmentioning

confidence: 99%

“…Besides Affect Colour Bar [LSL03], Semantize [WLM*14], and Lexical Episode Plots [GREA15] discussed above, Li and Ren [LR09] visualize 2D/3D heatmap visualizations of emotion matrices for individual text documents. Attitude Radial Plot by Almutairi [Alm13] displays a document as a 2D/3D map using the dimensions of affect, judgement, and appreciation as well as the position of text fragments in the document. The technique by Bembenik and Andruszkiewicz [BA16] takes an unstructured text document as input, extracts proposals and arguments alongside their polarity, and visualizes this data using a node‐link diagram.…”

Section: Sentiment Visualization Techniquesmentioning

confidence: 99%

“…Stance Analysis to represent analyses that encompass not only sentiment/affect, but also other categories of subjectivity/evaluation expressed in text, for instance, certainty or judgement. Currently, very few techniques support this task (6%), including the works of Small [Sma11], Almutairi [Alm13], Neviarouskaya et al [NAPI14], and Bembenik and Andruszkiewicz [BA16] certain topic besides the expressed sentiment. Finally, El-Assady et al [EAGA*16] provide an animation-based visualization of conversation transcripts, e.g.…”

Section: Finally We Have Introduced the Categorymentioning

confidence: 99%

See 3 more Smart Citations

The State of the Art in Sentiment Visualization

Kucher

Paradis

Kerren

2017

Computer Graphics Forum

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Section: Sentiment Visualization Techniquesmentioning

confidence: 99%

Section: Sentiment Visualization Techniquesmentioning

confidence: 99%

Section: Sentiment Visualization Techniquesmentioning

confidence: 99%

Section: Finally We Have Introduced the Categorymentioning

confidence: 99%

See 2 more Smart Citations

The State of the Art in Sentiment Visualization

Kucher

Paradis

Kerren

2017

Computer Graphics Forum

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“…This would rework the arbitrary privileging of axes in Figures 9 through 11 as a question of perspective, in relation to a discourse analyst's concerns. For recent developments in representation moving beyond the affordances of a 2-dimensional diagram on page or screen see Almutairi 2013, Zappvigna 2011.…”

Section: Gical Perspectivementioning

confidence: 99%

The Discourse Semantics of Attitudinal Relations: Continuing the Study of Lexis

Martin¹

2017

Vestn. Ross. univ. dru?by nar., Ser. Lingvist.

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Abstract. This paper explores some aspects of the problem of categorizing attitudinal relations in English, as part of a description of evaluation informed by systemic functional linguistics (SFL) -APPRAISAL. It reviews paradigmatic and syntagmatic orientations to lexis within this tradition, and the development of typological and topological representations of systemic relations. Corpus based argumentation is considered in relation to work on evaluation by Bednarek 2008; and proposals for continuing the study of lexis are suggested, focusing on resources for negotiating sadness and negative reactions to behavior (e.g. embarrassed, ashamed) and the affordances of topological representation. The paper highlights the possibilities and challenges involved in continuing the study of lexis in descriptions using SFL as their informing theory.

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Active Learning and Visual Analytics for Stance Classification with ALVA

Kucher

Paradis

Sahlgren

et al. 2017

ACM Trans. Interact. Intell. Syst.

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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 stance classifier for applications such as social media monitoring.

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Visualizing patterns of appraisal in texts and corpora

Cited by 9 publications

References 0 publications

The State of the Art in Sentiment Visualization

The State of the Art in Sentiment Visualization

The Discourse Semantics of Attitudinal Relations: Continuing the Study of Lexis

Active Learning and Visual Analytics for Stance Classification with ALVA

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