24 citations · 66 across the 11 of their papers we have counts for
6 papers · 1 filter
Learning Emotion from 100 Observations: Unexpected Robustness of Deep Learning under Strong Data Limitations
Sven Buechel, João Sedoc, H. Andrew Schwartz +1
One of the major downsides of Deep Learning is its supposed need for vast amounts of training data. As such, these techniques appear ill-suited for NLP areas where annotated data i…
Modeling Empathy and Distress in Reaction to News Stories
Sven Buechel, Anneke Buffone, Barry Slaff +2
Computational detection and understanding of empathy is an important factor in advancing human-computer interaction. Yet to date, text-based empathy prediction has the following ma…
Representation Mapping: A Novel Approach to Generate High-Quality Multi-Lingual Emotion Lexicons
Sven Buechel, Udo Hahn
In the past years, sentiment analysis has increasingly shifted attention to representational frameworks more expressive than semantic polarity (being positive, negative or neutral)…
JeSemE: A Website for Exploring Diachronic Changes in Word Meaning and Emotion
Johannes Hellrich, Sven Buechel, Udo Hahn
We here introduce a substantially extended version of JeSemE, an interactive website for visually exploring computationally derived time-variant information on word meanings and le…
Emotion Representation Mapping for Automatic Lexicon Construction (Mostly) Performs on Human Level
Sven Buechel, Udo Hahn
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 annot…
Modeling Word Emotion in Historical Language: Quantity Beats Supposed Stability in Seed Word Selection
Johannes Hellrich, Sven Buechel, Udo Hahn
To understand historical texts, we must be aware that language -- including the emotional connotation attached to words -- changes over time. In this paper, we aim at estimating th…