| Challenge: | Several datasets have been annotated and published for classification of emotions. |
| Approach: | They aggregated emotion corpora in a common file format with a shared annotation schema . they perform cross-corpus classification experiments to gain insight and a better understanding of differences . |
| Outcome: | The proposed model can be trained on a subset of corpora, but not on all corporata. |
Similar Papers
An Emotional Mess! Deciding on a Framework for Building a Dutch Emotion-Annotated Corpus (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing frameworks for emotion recognition are limited and do not allow for categorical versus dimensional oppositions. |
| Approach: | They propose to use the emotions joy, love, anger, sadness and fear as well as dimensional models to annotate texts from different domains and topics. |
| Outcome: | The proposed frameworks are well-suited to annotate texts from different domains and topics, but the connotation of the labels strongly depends on the origin of the texts. |
A Comparison Of Emotion Annotation Schemes And A New Annotated Data Set (L18-1)
Copied to clipboard
| Challenge: | a series of study on positive/negative sentiments has been conducted on tweets, but recognition of more nuanced affect has received little attention . valence, arousal, dominance and surprise are the most commonly used emotion representation schemes . |
| Approach: | They propose to annotate tweets with scores on four emotion dimensions . they compare annotator agreement with relative annotation schemes over categorical ones . |
| Outcome: | The proposed model improves agreement with relative annotation schemes over categorical ones on Ekman's six basic emotions. |
A (Psycho-)Linguistically Motivated Scheme for Annotating and Exploring Emotions in a Genre-Diverse Corpus (2022.lrec-1)
Copied to clipboard
| Challenge: | Using a linguistic perspective, emotion annotation is considered a difficult task because of the lack of consensus on emotional categories, the fuzziness of boundaries between them or the great variability of emotion expressions types. |
| Approach: | They propose a scheme for emotion annotation and its manual application on a genre-diverse corpus of texts written in french. |
| Outcome: | The proposed method clarifies the main concepts implied by the analysis of emotions as they are expressed in texts and performs a manual annotation campaign on a corpus of 1,594 texts (ca. 515K tokens) of different genres. |
Annotated Corpus for Sentiment Analysis in Odia Language (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing sentiment analysis models are not available for Odia 1 as it is a resource-poor language. |
| Approach: | They create an annotated Odia corpus and test its usability by training and testing on the corpus using various classifiers. |
| Outcome: | The created corpus contains 2045 Odia sentences from news domain annotated with sentiment labels using a well-defined annotation scheme. |
Understanding Emotions: A Dataset of Tweets to Study Interactions between Affect Categories (L18-1)
Copied to clipboard
| Challenge: | a new dataset is used to classify text into positive, negative, and neutral classes . a large amount of work on automatic detecting emotions from text has focused on classifying text into basic emotion categories . |
| Approach: | They use Twitter as the source of the textual data they annotate to find out which emotions often present together in tweets . |
| Outcome: | The proposed dataset is useful for training and testing supervised machine learning algorithms . it is based on the results of the SemEval-2018 task 1: Affect in Tweets . |
Angry or Sad ? Emotion Annotation for Extremist Content Characterisation (2022.lrec-1)
Copied to clipboard
| Challenge: | Social platforms play an increasingly important role in the propagation of extremist ideas. |
| Approach: | They propose to use a linguistic annotation scheme to characterize extremist content in French . they validate the scheme and test its ability to capture various aspects of emotions . |
| Outcome: | The proposed method combines sociological and linguistic knowledge to characterize extremist content in French. |
Who Feels What and Why? Annotation of a Literature Corpus with Semantic Roles of Emotions (C18-1)
Copied to clipboard
| Challenge: | Emotion analysis and classification is a challenging task which has been tackled with relatively straight-forward approaches. |
| Approach: | They propose to annotate emotion trigger phrases and entities in the roles of experiencers, targets, and causes of the emotion in literature by Project Gutenberg. |
| Outcome: | The proposed corpus supports qualitative literary studies and digital humanities. |
Sentence and Clause Level Emotion Annotation, Detection, and Classification in a Multi-Genre Corpus (L18-1)
Copied to clipboard
| Challenge: | Existing methods for predicting emotion categories are limited due to their multi-label nature . e.g. anger, joy, sadness are difficult to predict due to inherent multi-genre nature - a problem that is often overlooked in single-genrete text. |
| Approach: | They propose to expand existing annotated data to include 8 emotions from Plutchik's Wheel of Emotions . they explore the effectiveness of clause annotation in sentence-level emotion detection and classification . |
| Outcome: | The proposed system is the first to target the clause level and provides emotion classification for movie reviews datasets. |
Exploring the Emotional Dimension of French Online Toxic Content (2024.lrec-main)
Copied to clipboard
| Challenge: | Emotion annotations can be used to analyze content and can be applied to content analysis. |
| Approach: | They propose to use a corpus annotation scheme to annotate three online data sets composed of extremist, sexist and hateful messages respectively. |
| Outcome: | The proposed method can provide new insights for content analysis and stronger empirical background for automatic content detection. |
EmoProgress: Cumulated Emotion Progression Analysis in Dreams and Customer Service Dialogues (2024.lrec-main)
Copied to clipboard
| Challenge: | Emotion analysis often involves categorization of isolated textual units, but these are parts of longer discourses, like dialogues or stories. |
| Approach: | They propose a novel annotation setup for emotion categorization corpora that allows to annotate the emotion up to the annotated sentence. |
| Outcome: | The proposed annotation setup allows to answer the question which emotion is presumably experienced at a specific moment in time. |