| Challenge: | Existing lexicons of affect only show coarse associations, but are not accurate as human-created ones. |
| Approach: | They propose to use a manually created affect intensity lexicon with real-valued intensity scores for anger, fear, joy, and sadness. |
| Outcome: | The lexicon has real-valued scores for anger, fear, joy, and sadness . anger, fears, and sad words have very similar VAD scores . |
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Guilt by Association: Emotion Intensities in Lexical Representations (2021.emnlp-main)
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| Challenge: | linguistic models have a higher correlation with human ground truth ratings than labeled data . word vectors have often been evaluated on standard word relatedness benchmarks . |
| Approach: | They propose to use unsupervised, supervised, and finally supervised methods to extract emotional associations from pretrained vectors and models. |
| Outcome: | The proposed method shows higher correlation with ground truth ratings than state-of-the-art lexicons based on labeled data. |
Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 English Words (P18-1)
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| Challenge: | Words play a central role in language and thought. |
| Approach: | They propose a Lexicon with ratings of valence, arousal, and dominance for 20,000 words . they use Best–Worst Scaling to obtain fine-grained scores . |
| Outcome: | The proposed Lexicon has human ratings of valence, arousal, and dominance for 20,000 words . the ratings are more reliable than those in existing lexicons, the authors show . |
Learning and Evaluating Emotion Lexicons for 91 Languages (2020.acl-main)
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| Challenge: | Emotion lexicons describe the affective meaning of words but are limited in coverage for most languages. |
| Approach: | They propose a method for creating arbitrarily large emotion lexicons for any target language. |
| Outcome: | The proposed method exceeds human reliability for some languages and variables. |
Understanding Emotions: A Dataset of Tweets to Study Interactions between Affect Categories (L18-1)
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| 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 . |
Best Practices in the Creation and Use of Emotion Lexicons (2023.findings-eacl)
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| Challenge: | Inappropriate and incorrect use of emotion lexicons can lead to harmful inferences . |
| Approach: | They propose to present some of the practical and ethical considerations involved in the creation and use of emotion lexicons. |
| Outcome: | The proposed lexicons can lead to harmful inferences and sub-optimal results . the aim is to provide a comprehensive set of practical and ethical considerations . |
Aff2Vec: Affect–Enriched Distributional Word Representations (C18-1)
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| Challenge: | Affective word distributions are not well understood in literature. |
| Approach: | They propose a model that embeds affective word interpretations into enriched word embeddings. |
| Outcome: | The proposed model outperforms the state-of-the-art in word-similarity tasks and in emotion analysis, personality detection, and frustration prediction tasks. |
ELAL: An Emotion Lexicon for the Analysis of Alsatian Theatre Plays (2022.lrec-1)
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| Challenge: | a novel and manually corrected emotion lexicon is presented for Alsatian dialects . the dialects are used mainly orally and lack a stable and consensual spelling convention . |
| Approach: | They propose a novel and manually corrected emotion lexicon for Alsatian dialects . they use graphical variants of Alsalian lexical items to perform automatic emotion analysis . |
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Representation Mapping: A Novel Approach to Generate High-Quality Multi-Lingual Emotion Lexicons (L18-1)
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| Challenge: | Existing representational frameworks for emotion encoding are incompatible with semantic polarity, resulting in a large amount of incompatible emotion lexicons. |
| Approach: | They propose to map different emotion representation formats onto each other for mutual compatibility and interoperability of language resources. |
| Outcome: | The proposed method produces (near-)gold quality emotion lexicons even in crosslingual settings. |
Cross-Lingual Emotion Lexicon Induction using Representation Alignment in Low-Resource Settings (2020.coling-main)
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| Challenge: | Emotion lexicons provide information about associations between words and emotions. |
| Approach: | They use crowdsourcing to annotate words with Plutchik's 8 basic emotions, providing binary labels. |
| Outcome: | The proposed lexicons provide information about associations between words and emotions . the lexiconics are useful in emotional analyses of reviews, literary texts, and posts on social media . |
Evaluating Emotion Arcs Across Languages: Bridging the Global Divide in Sentiment Analysis (2023.findings-emnlp)
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| Challenge: | Emotion arcs capture how an individual (or a population) feels over time. |
| Approach: | They compare machine-learning and Lexicon-Only methods to generate emotion arcs . they run experiments on 18 diverse datasets in 9 languages . |
| Outcome: | The proposed method is poor at instance level emotion classification, but highly accurate when aggregating information from hundreds of instances. |