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. |
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| Challenge: | Emotion lexicons describe the affective meaning of words but are limited in coverage for most languages. |
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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. |
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| Challenge: | Emotion lexicons provide information about associations between words and emotions. |
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Odi et Amo. Creating, Evaluating and Extending Sentiment Lexicons for Latin. (2020.lrec-1)
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| Challenge: | a new paper aims to provide sentiment analysis tools for ancient languages . the current sentiment analysis resources only cover modern languages based on textual typologies . |
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Emotion Representation Mapping for Automatic Lexicon Construction (Mostly) Performs on Human Level (C18-1)
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| Challenge: | Emotion Representation Mapping (ERM) is an alternative to Word Emotion Induction (WEI) for automatic emotion lexicon construction. |
| Approach: | They propose a neural network approach to ERM that converts existing emotion ratings from one representation format into another by mapping Valence-Arousal-Dominance annotations into Ekman’s Basic Emotions. |
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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 . |
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| Challenge: | Since several decades emotional databases have been recorded by various laboratories. |
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| Challenge: | Existing frameworks for emotion recognition are limited and do not allow for categorical versus dimensional oppositions. |
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EmoEvent: A Multilingual Emotion Corpus based on different Events (2020.lrec-1)
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| Challenge: | In recent years, emotion detection in text has become more popular due to its potential applications in fields such as psychology, marketing, political science, among others. |
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