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Embedding Learning of Figurative Phrases for Emotion Classification in Micro-Blog Texts

Authors:

Shreshtha Mundra,

Sandya Mannarswamy,

Anirban SenAuthors Info & Claims

CODS '17: Proceedings of the 4th ACM IKDD Conferences on Data Sciences

Article No.: 3, Pages 1 - 9

https://doi.org/10.1145/3041823.3041828

Published: 09 March 2017 Publication History

Abstract

Figurative phrases such as idioms are a type of Multi-Word Expressions (MWE) that possess a specialized meaning, which is independent and different from the literal meaning of the constituent words. Figurative language is widely used to express emotions and are very predominant in micro-blog data.Therefore, an efficient model of emotion categorization for micro-blogs should be able to correctly represent the instances of figurative phrases in the data. However, due to their non-compositional nature, the phrasal representation of figurative language cannot be directly obtained from the constituent words and hence this requires novel approaches for addressing the problem of modeling figurative phrases in micro-blogs. Most of the existing methods of modeling figurative idiomatic phrases in traditional text data use the broader textual context available for better results. However, in case of micro-blog data, such large context is not available due to very short length of text, which poses an additional challenge. Given the need to model figurative language for emotion classification, this paper develops the novel idea of Emotion Sensitive Figurative Phrase Embedding (ESFPE) to model idiomatic phrases in micro-blog data and show upto 14% improvement in emotion classification performance over baseline. To the best of our knowledge, this is the first work towards figurative phrase modeling for emotion classification in micro-blog text.

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Cited By

Tan KLim TTan C(2021)A Study on Multiword Expression Features in Emotion Detection of Code-Mixed Twitter Data2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)10.1109/IICAIET51634.2021.9573850(1-5)Online publication date: 13-Sep-2021
https://doi.org/10.1109/IICAIET51634.2021.9573850
Resende de Mendonça RFelix de Brito Dde Franco Rosa Fdos Reis JBonacin R(2020)A Framework for Detecting Intentions of Criminal Acts in Social Media: A Case Study on TwitterInformation10.3390/info1103015411:3(154)Online publication date: 12-Mar-2020
https://doi.org/10.3390/info11030154

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CODS '17: Proceedings of the 4th ACM IKDD Conferences on Data Sciences

March 2017

136 pages

ISBN:9781450348461

DOI:10.1145/3041823

Copyright © 2017 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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SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
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Published: 09 March 2017

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CODS '17

CODS '17: Fourth ACM IKDD Conferences on Data Sciences

March 9 - 11, 2017

Chennai, India

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Cited By

Tan KLim TTan C(2021)A Study on Multiword Expression Features in Emotion Detection of Code-Mixed Twitter Data2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)10.1109/IICAIET51634.2021.9573850(1-5)Online publication date: 13-Sep-2021
https://doi.org/10.1109/IICAIET51634.2021.9573850
Resende de Mendonça RFelix de Brito Dde Franco Rosa Fdos Reis JBonacin R(2020)A Framework for Detecting Intentions of Criminal Acts in Social Media: A Case Study on TwitterInformation10.3390/info1103015411:3(154)Online publication date: 12-Mar-2020
https://doi.org/10.3390/info11030154

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