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Supervised Lexicon Extraction for Emotion Classification

Authors:

Alberto Purpura,

Chiara Masiero,

Gianmaria Silvello,

Gian Antonio SustoAuthors Info & Claims

WWW '19: Companion Proceedings of The 2019 World Wide Web Conference

Pages 1071 - 1078

https://doi.org/10.1145/3308560.3316700

Published: 13 May 2019 Publication History

Abstract

Emotion Classification (EC) aims at assigning an emotion label to a textual document with two inputs – a set of emotion labels (e.g. anger, joy, sadness) and a document collection. The best performing approaches for EC are dictionary-based and suffer from two main limitations: (i) the out-of-vocabulary (OOV) keywords problem and (ii) they cannot be used across heterogeneous domains. In this work, we propose a way to overcome these limitations with a supervised approach based on TF-IDF indexing and Multinomial Linear Regression with Elastic-Net regularization to extract an emotion lexicon and classify short documents from diversified domains. We compare the proposed approach to state-of-the-art methods for document representation and classification by running an extensive experimental study on two shared and heterogeneous data sets.

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Cited By

Hidayatullah A(2024)Parameter Efficient Fine-tuning using Low-Rank Adaptation for Emotion Classification in Indonesian Texts2024 9th International Conference on Information Technology and Digital Applications (ICITDA)10.1109/ICITDA64560.2024.10810037(1-6)Online publication date: 7-Nov-2024
https://doi.org/10.1109/ICITDA64560.2024.10810037
Bolpagni MBroglio MInnocenzi AUlivieri T(2024)EMOtivo: A Classifier for Emotion Detection of Italian Texts Trained on a Self-Labelled CorpusNew Frontiers in Textual Data Analysis10.1007/978-3-031-55917-4_3(29-40)Online publication date: 24-Sep-2024
https://doi.org/10.1007/978-3-031-55917-4_3
Feng SWei JWang DYang XYang ZZhang YYu G(2021)SINN: A speaker influence aware neural network model for emotion detection in conversationsWorld Wide Web10.1007/s11280-021-00954-824:6(2019-2048)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1007/s11280-021-00954-8
Show More Cited By

Index Terms

Supervised Lexicon Extraction for Emotion Classification

Index terms have been assigned to the content through auto-classification.

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cover image ACM Other conferences

WWW '19: Companion Proceedings of The 2019 World Wide Web Conference

May 2019

1331 pages

ISBN:9781450366755

DOI:10.1145/3308560

Editors:
Ling Liu
Georgia Tech, USA
,
Ryen White
Microsoft Research, USA

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

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Published: 13 May 2019

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WWW '19

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May 13 - 17, 2019

San Francisco, USA

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Cited By

Hidayatullah A(2024)Parameter Efficient Fine-tuning using Low-Rank Adaptation for Emotion Classification in Indonesian Texts2024 9th International Conference on Information Technology and Digital Applications (ICITDA)10.1109/ICITDA64560.2024.10810037(1-6)Online publication date: 7-Nov-2024
https://doi.org/10.1109/ICITDA64560.2024.10810037
Bolpagni MBroglio MInnocenzi AUlivieri T(2024)EMOtivo: A Classifier for Emotion Detection of Italian Texts Trained on a Self-Labelled CorpusNew Frontiers in Textual Data Analysis10.1007/978-3-031-55917-4_3(29-40)Online publication date: 24-Sep-2024
https://doi.org/10.1007/978-3-031-55917-4_3
Feng SWei JWang DYang XYang ZZhang YYu G(2021)SINN: A speaker influence aware neural network model for emotion detection in conversationsWorld Wide Web10.1007/s11280-021-00954-824:6(2019-2048)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1007/s11280-021-00954-8
Li RSi QFu PLin ZWang WShi G(2019)A Multi-channel Neural Network for Imbalanced Emotion Recognition2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI.2019.00057(353-360)Online publication date: Nov-2019
https://doi.org/10.1109/ICTAI.2019.00057

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