Search Results for author:
Found 14 papers, 1 papers with code
“splink” is happy and “phrouth” is scary: Emotion Intensity Analysis for Nonsense Words
This raises the question if the association is purely a product of the learned affective imports inherent to semantic meanings, or is also an effect of other features of words, e. g., morphological and phonological patterns.
SemEval 2022 Task 10: Structured Sentiment Analysis
In this paper, we introduce the first SemEval shared task on Structured Sentiment Analysis, for which participants are required to predict all sentiment graphs in a text, where a single sentiment graph is composed of a sentiment holder, target, expression and polarity.
Experiencer-Specific Emotion and Appraisal Prediction
Emotion classification in NLP assigns emotions to texts, such as sentences or paragraphs.
Dimensional Modeling of Emotions in Text with Appraisal Theories: Corpus Creation, Annotation Reliability, and Prediction
We analyze the suitability of appraisal theories for emotion analysis in text with the goal of understanding if appraisal concepts can reliably be reconstructed by annotators, if they can be predicted by text classifiers, and if appraisal concepts help to identify emotion categories.
x-enVENT: A Corpus of Event Descriptions with Experiencer-specific Emotion and Appraisal Annotations
In addition, we link them to the event they found salient (which can be different for different experiencers in a text) by annotating event properties, or appraisals (e. g., the perceived event undesirability, the uncertainty of its outcome).
"splink" is happy and "phrouth" is scary: Emotion Intensity Analysis for Nonsense Words
This raises the question if the association is purely a product of the learned affective imports inherent to semantic meanings, or is also an effect of other features of words, e. g., morphological and phonological patterns.
From Theories on Styles to their Transfer in Text: Bridging the Gap with a Hierarchical Survey
With this paper, we aim at providing a comprehensive discussion of the styles that have received attention in the transfer task.
Emotion Ratings: How Intensity, Annotation Confidence and Agreements are Entangled
When humans judge the affective content of texts, they also implicitly assess the correctness of such judgment, that is, their confidence.
Emotion-Aware, Emotion-Agnostic, or Automatic: Corpus Creation Strategies to Obtain Cognitive Event Appraisal Annotations
Appraisal theories explain how the cognitive evaluation of an event leads to a particular emotion.
Lost in Back-Translation: Emotion Preservation in Neural Machine Translation
Machine translation provides powerful methods to convert text between languages, and is therefore a technology enabling a multilingual world.
Challenges in Emotion Style Transfer: An Exploration with a Lexical Substitution Pipeline
We propose the task of emotion style transfer, which is particularly challenging, as emotions (here: anger, disgust, fear, joy, sadness, surprise) are on the fence between content and style.
Appraisal Theories for Emotion Classification in Text
Automatic emotion categorization has been predominantly formulated as text classification in which textual units are assigned to an emotion from a predefined inventory, for instance following the fundamental emotion classes proposed by Paul Ekman (fear, joy, anger, disgust, sadness, surprise) or Robert Plutchik (adding trust, anticipation).
Crowdsourcing and Validating Event-focused Emotion Corpora for German and English
Sentiment analysis has a range of corpora available across multiple languages.
A Computational Exploration of Exaggeration
Several NLP studies address the problem of figurative language, but among non-literal phenomena, they have neglected exaggeration.
