Emotion Classification Corpora
Appraisal enISEAR: A reannotation of the enISEAR corpus with Cognitive Appraisal (2020, 2021)
We reannotate the enISEAR corpus with cognitive appraisal dimensions following the Smith/Ellsworth model. The corpus consists of 1001 English event descriptions, annotated with the emotion the event has been described for and the appraisal dimensions of pleasantness, insecurity, self- and situational control, attention, and effort.
Authors: Jan Hofmann, Enrica Troiano, Roman Klinger
- Data source: Self reports
- Annotation procedure: Postannotation
- Original Paper which introduces the concept of appraisal for emotion analysis.
- Paper with describes experiments on different annotation strategies.
- Data Download including different annotation strategies
- Repository with code and data
deISEAR, enISEAR: Self-reports of events associated with given emotions (2019)
deISEAR and enISEAR are a German and an English corpus created in the spirit of the original ISEAR data set, but via crowdsourcing in a two-step procedure, to ensure quality. The corpora consist of 1001 event descriptions which are associated with a predefined emotion.
Authors: Enrica Troiano, Sebastian Pado, Roman Klinger
Unified Emotions (2018)
Several emotion corpora exist nowadays, many in different file formats and with different label sets. We aggregated these corpora with an automatic download and conversion pipeline such that these resources are easier to be used and compared.
Authors: Laura Bostan, Roman Klinger
Implicit Emotions Shared Task (2018)
For this shared task which took place with WASSA 2018, we collected data to have similar properties as the ISEAR data, but via distant supervision on Twitter. These data therefore mainly consist of event description without the explicit mention of an emotion word.
The test data is freely available. Contact me for a password to directly access the training data.
Authors: Roman Klinger, Saif Mohammad, Alexandra Balahur, Veronique Hoste, Orphee de Clercq
SSEC Corpus: Annotation of SemEval 2016 Stance Sentiment Corpus with Emotions (2018)
We reannotated the existing SemEval 2016 corpus, a resource already labeled with stances and sentiment, with emotions in a multiclass setting. This enables comparisons of these annotation layers. We publish all annotations of all annotators.
Authors: Hendrik Schuff, Jeremy Barnes, Julian Mohme, Sebastian Pado, Roman Klinger
Emotion Analysis from Text and Images (2017)
Emotion analysis in social media might need to consider images together with the text which refers to them, for instance on Twitter. For analyzing this complementarity, we collected a corpus of Tweets which contain images. It is automatically labeled based on hashtags. We only provide Tweet-IDs. If you need help with downloading the corresponding data via the Twitter API, contact us.
Author: Roman Klinger
Relational Emotion Corpora
Emotion relation corpus for the recognition of emotional relations of characters in fan fiction (2019)
Semantic role labeling of emotion events is a challenging task. In this corpus, we simplify this to a binary relation extraction task, in which character mention pairs are labeled with directed emotional relations between them, i.e., a character is either an emotion experiencer or the cause of an emotion.
Authors: Evgeny Kim, Roman Klinger
Emotion Communication Channels (2019)
The author of fictional texts can decide to let the character of a story to express in emotions in different ways, for instance by facial expressions, body movements, voice. With this corpus, we provide a resource in which we annotated these communication channels. This corpus is an extension of the emotion relation corpus mentioned above.
Authors: Evgeny Kim, Roman Klinger
REMAN and GoodNewsEveryone: Emotion Corpora for Semantic Roles of Emotion Events (2019)
Emotions are commonly expressed in context of a mention of an experiencer (which can be the author of a text), with specific trigger words, and can describe the target and the stimulus of the emotion. We publish two corpora with such annotations, one of literature from Project Gutenberg and one of news headlines (additionally annotated with the reader perspective of emotions).
Authors: Laura Bostan, Evgeny Kim, Roman Klinger
Corpus 1: REMAN
Corpus 2: GoodNewsEveryone
Resources and Dictionaries for Emotion Analysis
IMS Participation in EmoInt 2018
We participated in the shared task on emotion intensity prediction at WASSA in 2018 and scored second. Our model and results consist of a comparably standard neural architecture informed with different dictionaries of emotions, abstractness, concreteness, valence, arousal. We make all these resources and our implementation available.
Authors: Maximilian Koeper, Evgeny Kim, Roman Klinger
German Emotion Dictionaries created for the Analysis of Franz Kafka's Texts (2016)
We manually created German dictionaries for emotion analysis in Kafka’s Schloss and Amerika. These dictionaries are more specific than general dictionaries and might perform worse on other texts, however, they might be a good starting point for related text analyses.
Authors: Roman Klinger, Surayya Samat Suliya
Irony, Sarcasm and Satire
Twitter Corpus to compare irony to sarcasm (2016)
The concepts of irony and sarcasm are often used interchangeably, though they are not the same. With this corpus (and paper), we analze if a difference between these concepts can empirically be found on Twitter. We publish the Tweets themselves, together with meta information.
Authors: Jennifer Ling, Roman Klinger
German Satire Detection Corpus (2019)
We publish the first German corpus for satire detection. It is also the first corpus available with the information from which source an article came which enables training models with adversarial methods to not overfit to such confounding variables.
Authors: Robert McHardy, Heike Adel, Roman Klinger
Resources for Sentiment Analysis, Opinion Mining, and Hate Speech Detection
Stance/HOF in the US 2020 Elections (2021)
This corpus consists of Tweets from the US 2020 elections and is annotated for hate-speech and stance towards the two main candidates.
Authors:Lara Grimminger, Roman Klinger
- Data source: Twitter
- Annotation procedure: Experts
- Paper will be available soon
SCARE: German Corpus for Aspect-based Sentiment Analysis in App-Reviews (2016)
There are not many resources for aspect-based sentiment analysis in German. We contribute a corpus of Google Play reviews annotated with subjective phrases, aspects, and their relation.
Authors: Mario Saenger, Roman Klinger