Domain Adaptation Using Domain Similarity- and Domain Complexity-Based Instance Selection for Cross-Domain Sentiment Analysis

2012 ◽  
Author(s):  
Robert Remus
Keyword(s):  
Sentiment Analysis ◽  
Domain Adaptation ◽  
Instance Selection ◽  
Cross Domain ◽  
Selection For ◽  
Domain Similarity

scholarly journals Domain Adaptation and Instance Selection for Disease Syndrome Classification over Veterinary Clinical Notes

2020 ◽  
Author(s):  
Brian Hur ◽  
Timothy Baldwin ◽  
Karin Verspoor ◽  
Laura Hardefeldt ◽  
James Gilkerson
Keyword(s):  
Domain Adaptation ◽  
Instance Selection ◽  
Clinical Notes ◽  
Selection For ◽  
Syndrome Classification

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.

scholarly journals A Novel Domain Adaptation-Based Intelligent Fault Diagnosis Model to Handle Sample Class Imbalanced Problem

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3382
Author(s):  
Zhongwei Zhang ◽  
Mingyu Shao ◽  
Liping Wang ◽  
Sujuan Shao ◽  
Chicheng Ma
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Class Imbalance ◽  
Rolling Bearing ◽  
Industrial Applications ◽  
Mechanical Equipment ◽  
Reliable Operation ◽  
Cross Domain ◽  
Geometric Difference ◽  
Model Generalization

As the key component to transmit power and torque, the fault diagnosis of rotating machinery is crucial to guarantee the reliable operation of mechanical equipment. Regrettably, sample class imbalance is a common phenomenon in industrial applications, which causes large cross-domain distribution discrepancies for domain adaptation (DA) and results in performance degradation for most of the existing mechanical fault diagnosis approaches. To address this issue, a novel DA approach that simultaneously reduces the cross-domain distribution difference and the geometric difference is proposed, which is defined as MRMI. This work contains three parts to improve the sample class imbalance issue: (1) A novel distance metric method (MVD) is proposed and applied to improve the performance of marginal distribution adaptation. (2) Manifold regularization is combined with instance reweighting to simultaneously explore the intrinsic manifold structure and remove irrelevant source-domain samples adaptively. (3) The ℓ2-norm regularization is applied as the data preprocessing tool to improve the model generalization performance. The gear and rolling bearing datasets with class imbalanced samples are applied to validate the reliability of MRMI. According to the fault diagnosis results, MRMI can significantly outperform competitive approaches under the condition of sample class imbalance.

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