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Found 21 papers, 9 papers with code
WASSA 2021 Shared Task: Predicting Empathy and Emotion in Reaction to News Stories
This paper presents the results that were obtained from the WASSA 2021 shared task on predicting empathy and emotions.
Emotion Embeddings $\unicode{x2014}$ Learning Stable and Homogeneous Abstractions from Heterogeneous Affective Datasets
Human emotion is expressed in many communication modalities and media formats and so their computational study is equally diversified into natural language processing, audio signal analysis, computer vision, etc.
Empathic Conversations: A Multi-level Dataset of Contextualized Conversations
Hence, we collected detailed characterization of the participants' traits, their self-reported empathetic response to news articles, their conversational partner other-report, and turn-by-turn third-party assessments of the level of self-disclosure, emotion, and empathy expressed.
EmoBank: Studying the Impact of Annotation Perspective and Representation Format on Dimensional Emotion Analysis
We describe EmoBank, a corpus of 10k English sentences balancing multiple genres, which we annotated with dimensional emotion metadata in the Valence-Arousal-Dominance (VAD) representation format.
Towards Label-Agnostic Emotion Embeddings
Research in emotion analysis is scattered across different label formats (e. g., polarity types, basic emotion categories, and affective dimensions), linguistic levels (word vs. sentence vs. discourse), and, of course, (few well-resourced but much more under-resourced) natural languages and text genres (e. g., product reviews, tweets, news).
Learning and Evaluating Emotion Lexicons for 91 Languages
Emotion lexicons describe the affective meaning of words and thus constitute a centerpiece for advanced sentiment and emotion analysis.
Learning Word Ratings for Empathy and Distress from Document-Level User Responses
The underlying problem of learning word ratings from higher-level supervision has to date only been addressed in an ad hoc fashion and has not used deep learning methods.
A Time Series Analysis of Emotional Loading in Central Bank Statements
We examine the affective content of central bank press statements using emotion analysis.
Learning Emotion from 100 Observations: Unexpected Robustness of Deep Learning under Strong Data Limitations
One of the major downsides of Deep Learning is its supposed need for vast amounts of training data.
Modeling Empathy and Distress in Reaction to News Stories
Computational detection and understanding of empathy is an important factor in advancing human-computer interaction.
JeSemE: Interleaving Semantics and Emotions in a Web Service for the Exploration of Language Change Phenomena
We here introduce a substantially extended version of JeSemE, an interactive website for visually exploring computationally derived time-variant information on word meanings and lexical emotions assembled from five large diachronic text corpora.
JeSemE: A Website for Exploring Diachronic Changes in Word Meaning and Emotion
We here introduce a substantially extended version of JeSemE, an interactive website for visually exploring computationally derived time-variant information on word meanings and lexical emotions assembled from five large diachronic text corpora.
Representation Mapping: A Novel Approach to Generate High-Quality Multi-Lingual Emotion Lexicons
In the past years, sentiment analysis has increasingly shifted attention to representational frameworks more expressive than semantic polarity (being positive, negative or neutral).
A Corpus of Corporate Annual and Social Responsibility Reports: 280 Million Tokens of Balanced Organizational Writing
We introduce JOCo, a novel text corpus for NLP analytics in the field of economics, business and management.
Emotion Representation Mapping for Automatic Lexicon Construction (Mostly) Performs on Human Level
Emotion Representation Mapping (ERM) has the goal to convert existing emotion ratings from one representation format into another one, e. g., mapping Valence-Arousal-Dominance annotations for words or sentences into Ekman's Basic Emotions and vice versa.
Modeling Word Emotion in Historical Language: Quantity Beats Supposed Stability in Seed Word Selection
To understand historical texts, we must be aware that language -- including the emotional connotation attached to words -- changes over time.
Word Emotion Induction for Multiple Languages as a Deep Multi-Task Learning Problem
Predicting the emotional value of lexical items is a well-known problem in sentiment analysis.
Readers vs. Writers vs. Texts: Coping with Different Perspectives of Text Understanding in Emotion Annotation
We here examine how different perspectives of understanding written discourse, like the reader{'}s, the writer{'}s or the text{'}s point of view, affect the quality of emotion annotations.
Feelings from the Past---Adapting Affective Lexicons for Historical Emotion Analysis
We here describe a novel methodology for measuring affective language in historical text by expanding an affective lexicon and jointly adapting it to prior language stages.
