Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/162087
Title: | Tailored text augmentation for sentiment analysis | Authors: | Feng, Zijian Zhou, Hanzhang Zhu, Zixiao Mao, Kezhi |
Keywords: | Engineering::Electrical and electronic engineering | Issue Date: | 2022 | Source: | Feng, Z., Zhou, H., Zhu, Z. & Mao, K. (2022). Tailored text augmentation for sentiment analysis. Expert Systems With Applications, 205, 117605-. https://dx.doi.org/10.1016/j.eswa.2022.117605 | Journal: | Expert Systems with Applications | Abstract: | In synonym replacement-based data augmentation techniques for natural language processing tasks, words in a sentence are often sampled randomly with equal probability. In this paper, we propose a novel data augmentation technique named Tailored Text Argumentation (TTA) for sentiment analysis. It has two main operations. The first operation is the probabilistic word sampling for synonym replacement based on the discriminative power and relevance of the word to sentiment. The second operation is the identification of words irrelevant to sentiment but discriminative for the training data, and application of zero masking or contextual replacement to these words. The first operation expands the coverage of discriminative words, while the second operation alleviates the problem of misfitting. Both operations tend to improve the model's generalization capability. Extensive experiments on simulated low-data regimes demonstrate that TTA yields notable improvements over six strong baselines. Finally, TTA is applied to public sentiment analysis on measures against Covid-19, which again proves the effectiveness of the new data augmentation algorithm. | URI: | https://hdl.handle.net/10356/162087 | ISSN: | 0957-4174 | DOI: | 10.1016/j.eswa.2022.117605 | Schools: | School of Electrical and Electronic Engineering Interdisciplinary Graduate School (IGS) |
Rights: | © 2022 Elsevier Ltd. All rights reserved. | Fulltext Permission: | none | Fulltext Availability: | No Fulltext |
Appears in Collections: | EEE Journal Articles IGS Journal Articles |
SCOPUSTM
Citations
50
8
Updated on Mar 18, 2024
Web of ScienceTM
Citations
50
4
Updated on Oct 24, 2023
Page view(s)
161
Updated on Mar 18, 2024
Google ScholarTM
Check
Altmetric
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.