scholarly journals Unified Feature and Instance Based Domain Adaptation for Aspect-Based Sentiment Analysis

2020 ◽  
Author(s):  
Chenggong Gong ◽  
Jianfei Yu ◽  
Rui Xia
Keyword(s):  
Sentiment Analysis ◽  
Domain Adaptation

scholarly journals Adversarial Training Based Multi-Source Unsupervised Domain Adaptation for Sentiment Analysis

2020 ◽  
Vol 34 (05) ◽  
pp. 7618-7625
Author(s):  
Yong Dai ◽  
Jian Liu ◽  
Xiancong Ren ◽  
Zenglin Xu
Keyword(s):  
Sentiment Analysis ◽  
Domain Adaptation ◽  
State Of The Art ◽  
Weak Assumption ◽  
Target Domain ◽  
Smoothness Assumption ◽  
Unsupervised Domain Adaptation ◽  
Good Target ◽  
Adversarial Training ◽  
Learning Frameworks

Multi-source unsupervised domain adaptation (MS-UDA) for sentiment analysis (SA) aims to leverage useful information in multiple source domains to help do SA in an unlabeled target domain that has no supervised information. Existing algorithms of MS-UDA either only exploit the shared features, i.e., the domain-invariant information, or based on some weak assumption in NLP, e.g., smoothness assumption. To avoid these problems, we propose two transfer learning frameworks based on the multi-source domain adaptation methodology for SA by combining the source hypotheses to derive a good target hypothesis. The key feature of the first framework is a novel Weighting Scheme based Unsupervised Domain Adaptation framework ((WS-UDA), which combine the source classifiers to acquire pseudo labels for target instances directly. While the second framework is a Two-Stage Training based Unsupervised Domain Adaptation framework (2ST-UDA), which further exploits these pseudo labels to train a target private extractor. Importantly, the weights assigned to each source classifier are based on the relations between target instances and source domains, which measured by a discriminator through the adversarial training. Furthermore, through the same discriminator, we also fulfill the separation of shared features and private features.Experimental results on two SA datasets demonstrate the promising performance of our frameworks, which outperforms unsupervised state-of-the-art competitors.

scholarly journals Domain Adaptation with Category Attention Network for Deep Sentiment Analysis

2020 ◽  
Author(s):  
Dongbo Xi ◽  
Fuzhen Zhuang ◽  
Ganbin Zhou ◽  
Xiaohu Cheng ◽  
Fen Lin ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Domain Adaptation ◽  
Attention Network

scholarly journals Domain Adaptation for Arabic Cross-Domain and Cross-Dialect Sentiment Analysis from Contextualized Word Embedding

2021 ◽  
Author(s):  
Abdellah El Mekki ◽  
Abdelkader El Mahdaouy ◽  
Ismail Berrada ◽  
Ahmed Khoumsi
Keyword(s):  
Sentiment Analysis ◽  
Domain Adaptation ◽  
Word Embedding ◽  
Cross Domain

scholarly journals Semi-supervised domain adaptation and collaborative deep learning for dual sentiment analysis

2019 ◽  
Vol 1 (8) ◽  
Author(s):  
Savitha Mathapati ◽  
Ayesha Nafeesa ◽  
R. Tanuja ◽  
S. H. Manjula ◽  
K. R. Venugopal
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Domain Adaptation

scholarly journals Cross-Domain End-To-End Aspect-Based Sentiment Analysis with Domain-Dependent Embeddings

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yingjie Tian ◽  
Linrui Yang ◽  
Yunchuan Sun ◽  
Dalian. Liu
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
Learning Strategy ◽  
Domain Adaptation ◽  
Online Reviews ◽  
Domain Model ◽  
Fine Grained ◽  
Cross Domain ◽  
End To End ◽  
Public Datasets

With the development of sentiment analysis, studies have been gradually classified based on different researched candidates. Among them, aspect-based sentiment analysis plays an important role in subtle opinion mining for online reviews. It used to be treated as a group of pipeline tasks but has been proved to be analysed well in an end-to-end model recently. Due to less labelled resources, the need for cross-domain aspect-based sentiment analysis has started to get attention. However, challenges exist when seeking domain-invariant features and keeping domain-dependent features to achieve domain adaptation within a fine-grained task. This paper utilizes the domain-dependent embeddings and designs the model CD-E2EABSA to achieve cross-domain aspect-based sentiment analysis in an end-to-end fashion. The proposed model utilizes the domain-dependent embeddings with a multitask learning strategy to capture both domain-invariant and domain-dependent knowledge. Various experiments are conducted and show the effectiveness of all components on two public datasets. Also, it is also proved that as a cross-domain model, CD-E2EABSA can perform better than most of the in-domain ABSA methods.

E2SAM: Evolutionary ensemble of sentiment analysis methods for domain adaptation

2019 ◽  
Vol 480 ◽  
pp. 273-286 ◽  
Author(s):  
Miguel López ◽  
Ana Valdivia ◽  
Eugenio Martínez-Cámara ◽  
M. Victoria Luzón ◽  
Francisco Herrera
Keyword(s):  
Sentiment Analysis ◽  
Domain Adaptation ◽  
Analysis Methods
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