Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/36755
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Emotion Classification in Texts Over Graph Neural Networks: Semantic Representation is Better Than Syntactic
Author(s): Ameer, Iqra
Bölücü, Necva
Sidorov, Grigori
Can, Burcu
Contact Email: burcu.can@stir.ac.uk
Keywords: Semantics
Syntactics
Emotion recognition
Feature extraction
Deep learning
Social networking (online)
Graph neural networks
Issue Date: 31-May-2023
Date Deposited: 27-Nov-2024
Citation: Ameer I, Bölücü N, Sidorov G & Can B (2023) Emotion Classification in Texts Over Graph Neural Networks: Semantic Representation is Better Than Syntactic. <i>IEEE Access</i>, 11, pp. 56921-56934. https://doi.org/10.1109/access.2023.3281544
Abstract: Social media is a widely used platform that provides a huge amount of user-generated content that can be processed to extract information about users’ emotions. This has numerous benefits, such as understanding how individuals feel about certain news or events. It can be challenging to categorize emotions from text created on social media, especially when trying to identify several different emotions from a short text length, as in a multi-label classification problem. Most previous work on emotion classification has focused on deep neural networks such as Convolutional Neural Networks and Recurrent Neural Networks. However, none of these networks have used semantic and syntactic knowledge to classify multiple emotions from a text. In this study, semantic and syntactic aware graph attention networks were proposed to classify emotions from a text with multiple labels. We integrated semantic information in the graph attention network in the form of Universal Conceptual Cognitive Annotation and syntactic information in the form of dependency trees. Our extensive experimental results showed that our two models, UCCA-GAT (accuracy = 71.2) and Dep-GAT (accuracy = 68.7), were able to outperform the state-of-the-art performance on both the challenging SemEval-2018 E-c: Detecting Emotions (multi-label classification) English dataset (accuracy = 58.8) and GoEmotions dataset (accuracy = 65.9).
DOI Link: 10.1109/access.2023.3281544
Rights: This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
Licence URL(s): http://creativecommons.org/licenses/by-nc-nd/4.0/

Files in This Item:
File Description SizeFormat 
Emotion_Classification_in_Texts_Over_Graph_Neural_Networks_Semantic_Representation_is_Better_Than_Syntactic.pdfFulltext - Published Version2.2 MBAdobe PDFView/Open



This item is protected by original copyright



A file in this item is licensed under a Creative Commons License Creative Commons

Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

The metadata of the records in the Repository are available under the CC0 public domain dedication: No Rights Reserved https://creativecommons.org/publicdomain/zero/1.0/

If you believe that any material held in STORRE infringes copyright, please contact library@stir.ac.uk providing details and we will remove the Work from public display in STORRE and investigate your claim.