XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection

Öhman, Emily; Pàmies, Marc; Kajava, Kaisla; Tiedemann, Jörg

doi:10.48550/arxiv.2011.01612

Cited by 2 publications

(2 citation statements)

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“…Previous work on multilingual style predominantly focuses on training LMs to perform cross-lingual and multilingual style classification and style transfer. Key styles studied include formality (Briakou et al, 2021;Krishna et al, 2022;Rippeth et al, 2022) and emotion (Öhman et al, 2018;Lamprinidis et al, 2021;Öhman et al, 2020), with another body of work focusing on style-aware multilingual generation with any subset of chosen styles Garcia et al, 2021).…”

Section: Related Workmentioning

confidence: 99%

Comparing Styles across Languages

Havaldar,

Pressimone,

Wong

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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Understanding how styles differ across languages is advantageous for training both humans and computers to generate culturally appropriate text. We introduce an explanation framework to extract stylistic differences from multilingual LMs and compare styles across languages. Our framework (1) generates comprehensive style lexica in any language and (2) consolidates feature importances from LMs into comparable lexical categories. We apply this framework to compare politeness, creating the first holistic multilingual politeness dataset and exploring how politeness varies across four languages. Our approach enables an effective evaluation of how distinct linguistic categories contribute to stylistic variations and provides interpretable insights into how people communicate differently around the world.

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Section: Related Workmentioning

confidence: 99%

Comparing Styles across Languages

Havaldar,

Pressimone,

Wong

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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“…XED is a fine-grained multilingual emotion dataset introduced by Öhman et al (2020) . The collection comprises human-annotated Finnish (25 k) and English (30 k) sentences, as well as planned annotations for 30 other languages, bringing new resources to a variety of low-resource languages.…”

Section: Related Workmentioning

confidence: 99%

Multi-label emotion classification of Urdu tweets

Ashraf

Khan

Butt

et al. 2022

PeerJ Computer Science

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Urdu is a widely used language in South Asia and worldwide. While there are similar datasets available in English, we created the first multi-label emotion dataset consisting of 6,043 tweets and six basic emotions in the Urdu Nastalíq script. A multi-label (ML) classification approach was adopted to detect emotions from Urdu. The morphological and syntactic structure of Urdu makes it a challenging problem for multi-label emotion detection. In this paper, we build a set of baseline classifiers such as machine learning algorithms (Random forest (RF), Decision tree (J48), Sequential minimal optimization (SMO), AdaBoostM1, and Bagging), deep-learning algorithms (Convolutional Neural Networks (1D-CNN), Long short-term memory (LSTM), and LSTM with CNN features) and transformer-based baseline (BERT). We used a combination of text representations: stylometric-based features, pre-trained word embedding, word-based n-grams, and character-based n-grams. The paper highlights the annotation guidelines, dataset characteristics and insights into different methodologies used for Urdu based emotion classification. We present our best results using micro-averaged F1, macro-averaged F1, accuracy, Hamming loss (HL) and exact match (EM) for all tested methods.

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XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection

Cited by 2 publications

References 0 publications

Comparing Styles across Languages

Comparing Styles across Languages

Multi-label emotion classification of Urdu tweets

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