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http://bura.brunel.ac.uk/handle/2438/29422
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DC Field | Value | Language |
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dc.contributor.author | Zhao, Q | - |
dc.contributor.author | Wu, P | - |
dc.contributor.author | Lian, J | - |
dc.contributor.author | An, D | - |
dc.contributor.author | Li, M | - |
dc.date.accessioned | 2024-07-26T13:12:01Z | - |
dc.date.available | 2024-07-26T13:12:01Z | - |
dc.date.issued | 2024-06-18 | - |
dc.identifier | ORCiD: Qin Zhao https://orcid.org/0000-0001-7579-2004 | - |
dc.identifier | ORCiD: Jie Lian https://orcid.org/0000-0002-2005-2022 | - |
dc.identifier | ORCiD: Dongdong An https://orcid.org/0000-0002-1412-8182 | - |
dc.identifier | ORCiD: Mazhen Li https://orcid.org/0000-0002-0820-5487 | - |
dc.identifier.citation | Zhao, Q. et al. (2024) 'TaneNet: Two-Level Attention Network Based on Emojis for Sentiment Analysis',IEEE Access, 12, pp. 86106 - 86119. doi: 10.1109/ACCESS.2024.3416379. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/29422 | - |
dc.description.abstract | During online communication, users often use irregular and ambiguous words, and sometimes use irony to express sarcasm. These words are difficult to analyze through text analysis, which poses a significant challenge for text sentiment analysis. As a novel communication method, emojis have a significant correlation with user emotions. In this paper, we use emojis to analyze the sentiment of short texts. Firstly, we validate that user information can help reduce the uncertainty of some emojis and use this information to identify the polarity of emojis. Then, we generate emoji representations by merging positional information, semantic information, emotional information, and frequency of appearance. Furthermore, we propose TaneNet, a two-level attention network based on emojis, which combines clause vectors and emoji representations to study the impact of emojis on the emotions of each clause in the text. Empirical results on two real-world datasets demonstrate that TaneNet outperforms existing state-of-the-art methods | en_US |
dc.description.sponsorship | National Key Research and Development Program of China (Grant Number: 2022YFB4501704); 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62302308, U2142206, 62372300 and 61702333); Shanghai Sailing Program (Grant Number: 21YF1432900) | en_US |
dc.format.extent | 86106 - 86119 | - |
dc.format.medium | Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.rights | Copyright © 2024 The Authors. Published by Institute of Electrical and Electronics Engineers (IEEE). 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/ | - |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | - |
dc.subject | emojis | en_US |
dc.subject | attention mechanisms | en_US |
dc.subject | word embedding | en_US |
dc.subject | sentiment analysis | en_US |
dc.subject | neural network | en_US |
dc.title | TaneNet: Two-Level Attention Network Based on Emojis for Sentiment Analysis | en_US |
dc.type | Article | en_US |
dc.date.dateAccepted | 2024-06-10 | - |
dc.identifier.doi | https://doi.org/10.1109/ACCESS.2024.3416379 | - |
dc.relation.isPartOf | IEEE Access | - |
pubs.publication-status | Published | - |
pubs.volume | 12 | - |
dc.identifier.eissn | 2169-3536 | - |
dc.rights.license | https://creativecommons.org/licenses/by-nc-nd/4.0/legalcodse.en | - |
dc.rights.holder | The Authors | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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