Data Sets
Data sets, lexicons and other resources from my group and our collaborations. Each entry links to a copy on this server and to its original location.
Emotion Corpora for appraisal theories and emotion component process model
Mult2EMo: Multimodal Emotion Experience, Expression, and Perception in Social Media Posts
2026Multimodal (text and image) social media posts that crowdworkers donated and annotated themselves, together with the events that triggered the emotions. A subset is additionally annotated by readers, so the corpus captures both the authors’ and the readers’ perspectives. Access will be possible upon request.
Donate or Create? Emotion-labeled Multimodal Social Media Posts
2025Social media posts with images, each annotated with an emotion. The posts were collected with two strategies: participants either donated posts they had already published about an emotional event (Donate), or recalled such an event and wrote a new post about it (Create). A smaller subset contains participants’ most recent posts. The data is available upon request under CC BY-NC-SA 4.0.
EmoProgress: Cumulated Emotion Progression Analysis in Dreams and Customer Service Dialogues
2024We created this corpus to analyze emotions as the cumulate in dreams and customer service dialogues. The assigned labeles represent the emotion up until the respective part of the instance, in context of the previous content.
COPING: An Emotion and Coping Strategy Corpus Based on Role Playing
2024Crowdworkers role-played personalities that use different strategies to cope with their emotions in controversial discussions. The corpus contains the resulting texts with emotion and coping-strategy annotations; data and models are distributed via Hugging Face.
crowd-enVENT · Appraisal Theories for Dimensional Modelling of Emotions in Text
2023We created this corpus with emotion and appraisal dimensions with labels from two perspectives – the person who lived through a described event and readers who only have access to the text. Each text has been generated by asking people on Prolific to complete the sentence (for a given emotion): I felt [emotion] when/that/if…
x-enVENT · Experiencer-specific Emotion and Appraisal Annotation
2022We reannotate event descriptions with 22 appraisal dimensions and emotions, for each person mentioned in an event description. This enables joint modelling experiments across multiple people in an event and analyses that are person-specific.
CPM-Corpus · Emotion Component Process Model Reannotation of REMAN and TEC
2021We reannotate parts of the TEC corpus and the REMAN corpus following the emotion component process model by Scherer, namely that the emotion is communicated by describing an event appraisal, a bodily reaction, an action tendency, a subjective feeling or an expression.
When using this corpus, please make sure to also cite the original publications by Saif Mohammad on the TEC corpus and our REMAN corpus publication.
Appraisal-enISEAR · Appraisal enISEAR: A reannotation of the enISEAR corpus with Cognitive Appraisal
2021We 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.
Paper with experiments on annotation strategies (WASSA 2021)Data Download including different annotation strategiesRepository with code and data
Fanfic · Emotion Communication Channels
2019The 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.
Emotion Classification Corpora
Less is More corpus · Less Is More? Demographic Author Information in Emotion Classification of Ambiguous Text
2026Emotion annotations of 250 texts from crowd-enVENT by 500 annotators. One group saw each text together with demographic information about its author, the other group saw only the text.
Jointly Prompting for Multiple Emotion Theories
2026Code and additional data to reproduce the robustness experiments in which LLMs are prompted jointly for basic emotions, valence–arousal, and cognitive appraisals.
Emotional BackStories (EBS) · Emotional BackStories: Contextualizing Ambiguous Event Descriptions in Plausible Narratives
2026Automatically generated narratives (backstories) that provide plausible context for otherwise ambiguous event descriptions, to study how context changes the interpretation of emotions. The generated stories were evaluated with automatic and human annotation.
MMEmo: MultiModal Emotion Analysis on Reddit
2022The MMEmo Corpus is a corpus of Reddit posts which contains images and text for the emotion the posts express, an emotion stimulus category, and the relation between the image and the text.
PO-EMO: Aesthetic Emotions in German and English Poetry
2020Poems in German and English annotated line by line with aesthetic emotions, i.e., the emotions elicited in the reader rather than those expressed in the text. Each line can receive multiple labels to capture mixed emotions. The data is available in TEI P5 XML and in a tab-separated format.
deISEAR, enISEAR: Self-reports of events associated with given emotions
2019deISEAR and enISEAR (not to be confused with the original ISEAR corpora) 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.
Unified Emotions
2018Several 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.
SSEC Corpus: Annotation of SemEval 2016 Stance Sentiment Corpus with Emotions
2018We 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.
IEST · Implicit Emotions Shared Task
2018For 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.
Emotion Analysis from Text and Images
2017Emotion 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
Download Original location Paper
PreprintDownload: images with textDownload: images without text
Relational Emotion and Emotion Stimulus Corpora
GerSti: A German Emotion Stimulus Corpus of News Headlines
2021Emotion stimulus detection became a popular task in emotion analysis recently, but most resources are only available in Mandarin and English. We contribute a German resource of token-level emotion stimulus annotations in a novel German news headline corpus and perform cross-lingual experiments in which we train on an English corpus and apply the model on our German resource.
GoodNewsEveryone: Semantic Roles of Emotions in News Headlines
2020News headlines annotated with emotions and their semantic roles (experiencer, cue, target, and stimulus), additionally annotated with the reader perspective of emotions.
FanFic · Emotion relation corpus for the recognition of emotional relations of characters in fan fiction
2019Semantic 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.
REMAN: Relational Emotion Annotation of Literature
2018Emotions are commonly expressed in the 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. REMAN annotates these semantic roles of emotions in literature from Project Gutenberg.
Resources and Dictionaries for Emotion Analysis
German Affective Narrative Generation
2025Data and code for the adaptation of an English affective narrative generation approach to German, for contextualized emotion analysis.
AutoBWS: Automatic Best–Worst-Scaling Annotations for Emotion Intensity
2024Emotion intensity annotations of tweets created automatically with a large language model, comparing direct rating scale predictions, pairwise comparisons, and best–worst scaling.
Emotion Intensity Lexicon of Nonsense Words
2022The goal in this study was to understand if nonsense words are reliably attributed emotions of particular intensity. To study this, we asked annotators in a best-worst-scaling setup to assign emotion intensities to nonsense words.
IMS Participation in EmoInt 2017
2017We 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.
German Emotion Dictionaries created for the Analysis of Franz Kafka's Texts
2016We 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.
Irony, Sarcasm and Satire
German Satire Detection Corpus
2019We 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.
Twitter Corpus to compare irony to sarcasm
2016The 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.
Resources for Sentiment Analysis, Opinion Mining, Hate Speech Detection, Claim Detection, Fact Checking, Deception
Emo-DeFaBel: Emotion Category Annotations of Arguments by Humans and LLMs
2025Crowdsourced emotion category annotations of the German arguments in the DeFaBel corpus, together with annotations by large language models in zero-shot, one-shot, and chain-of-thought settings.
Science Communication Distortions
2024Distorted science communication harms individuals and society as it can lead to unhealthy behavior change and decrease trust in scientific institutions. Given the rapidly increasing volume of science communication in recent years, a fine-grained understanding of how findings from scientific publications are reported to the general public, and methods to detect distortions from the original work automatically, are crucial. This dataset contains 1,600 instances of scientific findings from academic papers paired with corresponding findings as reported in news articles and tweets annotated wrt. four characteristics: causality, certainty, generality and sensationalism.
Hierarchical Detox: Mitigating Identity Term Bias in Toxicity Detection
2024Code and data for correcting the learning of toxicity detection models by considering targeted identity terms at different levels of specificity.
Can Factual Statements be Deceptive? The DeFaBel Corpus of Belief-based Deception
2024A data set of German argumentative texts that are argue for or against a specific statement, independent of their own belief. If the author does not believe in the statement, it is marked as deceptive. Therefore, this corpus is the first resource (we are aware of) that combines factuality, belief, arguments, and deception.
BEAR-Fact: A Twitter dataset with fact-checking labels, evidence texts and entity and relation annotations
2024A dataset of tweets annotated with fact-checking labels, evidence texts and structured knowledge, i.e., biomedical entities and relations.
UniDecor: A Unified Deception Corpus for Cross-Corpus Deception Detection
2023We aggregated a set of existing deception dataset in a unified format and performed cross-corpus and in-corpus experiments.
CoVERT: A Corpus of Crowdsourced Fact-checking Verdicts for Biomedical COVID-19 Tweets
2022A corpus of 300 Twitter posts with claims about Covid-19. All tweets are annotated with crowdsourced fact-checking verdicts (supports, refutes, not enough info) and evidence texts supporting the verdicts.
Stance/HOF in the US 2020 Elections
2021This corpus consists of Tweets from the US 2020 elections and is annotated for hate-speech and stance towards the two main candidates.
Biomedical Claims in Social Media
2021This corpus consists of Tweets regarding a set of medical conditions. We annotated the Tweets for containing an argumentative claim (or not). If the claim is explicitly mentioned, we also mark the claim phrase.
SCARE: German Corpus for Aspect-based Sentiment Analysis in App-Reviews
2016There 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.
USAGE: German and English Corpora for Aspect-based Sentiment Analysis in Product Reviews
2014There are not many resources for aspect-based sentiment analysis in German. We contribute a corpus of Amazon reviews annotated with subjective phrases, aspects, and their relation.
Resources for Biomedical and Chemical Text Mining
BEAR: Biomedical Entities and Relations in Tweets
2022A dataset of 2100 Twitter posts annotated with 14 different types of biomedical entities (e.g., disease, treatment, risk factor, etc.) and 20 relation types (including caused, treated, worsens, etc.).
Corpus and resources for the detection of miRNA mentions in scientific text
2014Weakly labeled corpus for protein-protein and drug-drug interactions
2012Corpus for testing normalization of variation mentions
2011Corpus of Medline abstracts annotated with IUPAC entities
2009Corpus of Medline abstracts annotated with chemical entities
2008Other Resources
Fair Grid Beam Search
2026Source code for fair grid beam search, a variant of grid beam search for lexically constrained text generation that does not favor easy-to-satisfy constraints.
LLMs' Default Demographics in Annotation
2025Code and model predictions for the study of which human demographic groups large language models imitate when they annotate subjective phenomena in text.
Obituary Corpus Annotated for Logical Zones
2020Download Original location Paper
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