Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/92354
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | en_US |
dc.contributor | Department of Chinese and Bilingual Studies | en_US |
dc.creator | Xiang, R | en_US |
dc.creator | Li, J | en_US |
dc.creator | Wan, M | en_US |
dc.creator | Gu, J | en_US |
dc.creator | Lu, Q | en_US |
dc.creator | Li, W | en_US |
dc.creator | Huang, CR | en_US |
dc.date.accessioned | 2022-03-24T08:13:47Z | - |
dc.date.available | 2022-03-24T08:13:47Z | - |
dc.identifier.issn | 0950-7051 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/92354 | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | © 2021 Elsevier B.V. All rights reserved. | en_US |
dc.rights | © 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/. | en_US |
dc.rights | The following publication Xiang, R., et al. (2021). "Affective awareness in neural sentiment analysis." Knowledge-Based Systems 226: 107137 is available at https://dx.doi.org/10.1016/j.knosys.2021.107137. | en_US |
dc.subject | Affective knowledge | en_US |
dc.subject | Deep neural network | en_US |
dc.subject | Sentiment analysis | en_US |
dc.subject | Sentiment lexicon | en_US |
dc.title | Affective awareness in neural sentiment analysis | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 226 | en_US |
dc.identifier.doi | 10.1016/j.knosys.2021.107137 | en_US |
dcterms.abstract | Sentiment analysis is helpful to bestow ability of understanding human's attitude in texts on artificial intelligence systems. In this area, text sentiment is usually signaled by a few indicative words that convey affective meanings and arouse readers’ collective emotions. However, most existing sentiment analysis models have predominantly featured through neural network architectures with end-to-end training manner and limited awareness of affective knowledge, which, as a result, often fails to pinpoint the essential features for sentiment prediction. In this work, we present a novel approach for sentiment analysis by fusing external affective knowledge into neural networks. The affective knowledge is distilled from two sentiment lexicons grounded by two psychological theories, e.g., the Affect Control Theory and word affections in terms of Valence, Arousal, and Dominance. To examine the effects of affective knowledge over sentiment analysis, we conduct cross-dataset and cross-model experiments along with a detailed ablation analysis. Results show that our proposed method outperforms trendy neural networks in all the five benchmarks with consistent and significant improvement (1.4% Accuracy in average). Further discussions demonstrate that all affective attributes exhibit positive effects to model enhancement and our model is robust to the change of lexicon size. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Knowledge-based systems, 17 Aug. 2021, v. 226, 107137 | en_US |
dcterms.isPartOf | Knowledge-based systems | en_US |
dcterms.issued | 2021-08-17 | - |
dc.identifier.scopus | 2-s2.0-85106644633 | - |
dc.identifier.artn | 107137 | en_US |
dc.description.validate | 202203 bcvc | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | a1247 | - |
dc.identifier.SubFormID | 44320 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Xiang_Affective_Awareness_Neural.pdf | Pre-Published version | 2.43 MB | Adobe PDF | View/Open |
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