14,590 research outputs found
An Empirical Analysis of the Role of Amplifiers, Downtoners, and Negations in Emotion Classification in Microblogs
The effect of amplifiers, downtoners, and negations has been studied in
general and particularly in the context of sentiment analysis. However, there
is only limited work which aims at transferring the results and methods to
discrete classes of emotions, e. g., joy, anger, fear, sadness, surprise, and
disgust. For instance, it is not straight-forward to interpret which emotion
the phrase "not happy" expresses. With this paper, we aim at obtaining a better
understanding of such modifiers in the context of emotion-bearing words and
their impact on document-level emotion classification, namely, microposts on
Twitter. We select an appropriate scope detection method for modifiers of
emotion words, incorporate it in a document-level emotion classification model
as additional bag of words and show that this approach improves the performance
of emotion classification. In addition, we build a term weighting approach
based on the different modifiers into a lexical model for the analysis of the
semantics of modifiers and their impact on emotion meaning. We show that
amplifiers separate emotions expressed with an emotion- bearing word more
clearly from other secondary connotations. Downtoners have the opposite effect.
In addition, we discuss the meaning of negations of emotion-bearing words. For
instance we show empirically that "not happy" is closer to sadness than to
anger and that fear-expressing words in the scope of downtoners often express
surprise.Comment: Accepted for publication at The 5th IEEE International Conference on
Data Science and Advanced Analytics (DSAA), https://dsaa2018.isi.it
Modeling Empathy and Distress in Reaction to News Stories
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 major limitations: It underestimates the
psychological complexity of the phenomenon, adheres to a weak notion of ground
truth where empathic states are ascribed by third parties, and lacks a shared
corpus. In contrast, this contribution presents the first publicly available
gold standard for empathy prediction. It is constructed using a novel
annotation methodology which reliably captures empathy assessments by the
writer of a statement using multi-item scales. This is also the first
computational work distinguishing between multiple forms of empathy, empathic
concern, and personal distress, as recognized throughout psychology. Finally,
we present experimental results for three different predictive models, of which
a CNN performs the best.Comment: To appear at EMNLP 201
IEST: WASSA-2018 Implicit Emotions Shared Task
Past shared tasks on emotions use data with both overt expressions of
emotions (I am so happy to see you!) as well as subtle expressions where the
emotions have to be inferred, for instance from event descriptions. Further,
most datasets do not focus on the cause or the stimulus of the emotion. Here,
for the first time, we propose a shared task where systems have to predict the
emotions in a large automatically labeled dataset of tweets without access to
words denoting emotions. Based on this intention, we call this the Implicit
Emotion Shared Task (IEST) because the systems have to infer the emotion mostly
from the context. Every tweet has an occurrence of an explicit emotion word
that is masked. The tweets are collected in a manner such that they are likely
to include a description of the cause of the emotion - the stimulus.
Altogether, 30 teams submitted results which range from macro F1 scores of 21 %
to 71 %. The baseline (MaxEnt bag of words and bigrams) obtains an F1 score of
60 % which was available to the participants during the development phase. A
study with human annotators suggests that automatic methods outperform human
predictions, possibly by honing into subtle textual clues not used by humans.
Corpora, resources, and results are available at the shared task website at
http://implicitemotions.wassa2018.com.Comment: Accepted at Proceedings of the 9th Workshop on Computational
Approaches to Subjectivity, Sentiment and Social Media Analysi
- …