@inproceedings{gollapalli-etal-2020-ester,
title = "{EST}e{R}: Combining Word Co-occurrences and Word Associations for Unsupervised Emotion Detection",
author = "Gollapalli, Sujatha Das and
Rozenshtein, Polina and
Ng, See-Kiong",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.93",
doi = "10.18653/v1/2020.findings-emnlp.93",
pages = "1043--1056",
abstract = "Accurate detection of emotions in user- generated text was shown to have several applications for e-commerce, public well-being, and disaster management. Currently, the state-of-the-art performance for emotion detection in text is obtained using complex, deep learning models trained on domain-specific, labeled data. In this paper, we propose ESTeR , an unsupervised model for identifying emotions using a novel similarity function based on random walks on graphs. Our model combines large-scale word co-occurrence information with word-associations from lexicons avoiding not only the dependence on labeled datasets, but also an explicit mapping of words to latent spaces used in emotion-enriched word embeddings. Our similarity function can also be computed efficiently. We study a range of datasets including recent tweets related to COVID-19 to illustrate the superior performance of our model and report insights on public emotions during the on-going pandemic.",
}
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<abstract>Accurate detection of emotions in user- generated text was shown to have several applications for e-commerce, public well-being, and disaster management. Currently, the state-of-the-art performance for emotion detection in text is obtained using complex, deep learning models trained on domain-specific, labeled data. In this paper, we propose ESTeR , an unsupervised model for identifying emotions using a novel similarity function based on random walks on graphs. Our model combines large-scale word co-occurrence information with word-associations from lexicons avoiding not only the dependence on labeled datasets, but also an explicit mapping of words to latent spaces used in emotion-enriched word embeddings. Our similarity function can also be computed efficiently. We study a range of datasets including recent tweets related to COVID-19 to illustrate the superior performance of our model and report insights on public emotions during the on-going pandemic.</abstract>
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%0 Conference Proceedings
%T ESTeR: Combining Word Co-occurrences and Word Associations for Unsupervised Emotion Detection
%A Gollapalli, Sujatha Das
%A Rozenshtein, Polina
%A Ng, See-Kiong
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F gollapalli-etal-2020-ester
%X Accurate detection of emotions in user- generated text was shown to have several applications for e-commerce, public well-being, and disaster management. Currently, the state-of-the-art performance for emotion detection in text is obtained using complex, deep learning models trained on domain-specific, labeled data. In this paper, we propose ESTeR , an unsupervised model for identifying emotions using a novel similarity function based on random walks on graphs. Our model combines large-scale word co-occurrence information with word-associations from lexicons avoiding not only the dependence on labeled datasets, but also an explicit mapping of words to latent spaces used in emotion-enriched word embeddings. Our similarity function can also be computed efficiently. We study a range of datasets including recent tweets related to COVID-19 to illustrate the superior performance of our model and report insights on public emotions during the on-going pandemic.
%R 10.18653/v1/2020.findings-emnlp.93
%U https://aclanthology.org/2020.findings-emnlp.93
%U https://doi.org/10.18653/v1/2020.findings-emnlp.93
%P 1043-1056
Markdown (Informal)
[ESTeR: Combining Word Co-occurrences and Word Associations for Unsupervised Emotion Detection](https://aclanthology.org/2020.findings-emnlp.93) (Gollapalli et al., Findings 2020)
ACL