Coarse Alignment of Topic and Sentiment: A Unified Model for Cross-Lingual Sentiment Classification

Wang, Deqing; Jing, Baoyu; Lu, Chenwei; Wu, Junjie; Liu, Guannan; Du, Chenguang; Zhuang, Fuzhen

doi:10.1109/tnnls.2020.2979225

Cited by 25 publications

(17 citation statements)

References 31 publications

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“…Domain adaptation is an important application of transfer learning that attempts to generalize the models from source domains to unseen target domains [19], [21], [30], [31], [32], [33], [34], [35]. Adversarial training, inspired by the success of generative adversarial modeling [36], has been widely applied for promoting the learning of transfer features in image classification.…”

Section: Domain Adaptationmentioning

confidence: 99%

SODA: Detecting COVID-19 in Chest X-Rays With Semi-Supervised Open Set Domain Adaptation

Zhou

Jing

Wang

et al. 2022

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Due to the shortage of COVID-19 viral testing kits, radiology imaging is used to complement the screening process. Deep learning based methods are promising in automatically detecting COVID-19 disease in chest x-ray images. Most of these works first train a Convolutional Neural Network (CNN) on an existing large-scale chest x-ray image dataset and then fine-tune the model on the newly collected COVID-19 chest x-ray dataset, often at a much smaller scale. However, simple fine-tuning may lead to poor performance for the CNN model due to two issues, firstly the large domain shift present in chest x-ray datasets and secondly the relatively small scale of the COVID-19 chest x-ray dataset. In an attempt to address these two important issues, we formulate the problem of COVID-19 chest x-ray image classification in a semi-supervised open set domain adaptation setting and propose a novel domain adaptation method, Semi-supervised Open set Domain Adversarial network (SODA). SODA is designed to align the data distributions across different domains in the general domain space and also in the common subspace of source and target data. In our experiments, SODA achieves a leading classification performance compared with recent state-of-the-art models in separating COVID-19 with common pneumonia. We also present initial results showing that SODA can produce better pathology localizations in the chest x-rays.

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Section: Domain Adaptationmentioning

confidence: 99%

SODA: Detecting COVID-19 in Chest X-Rays With Semi-Supervised Open Set Domain Adaptation

Zhou

Jing

Wang

et al. 2022

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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“…Aspect terms extraction for feature formation has received significant attention from researchers as it remarkably enhances the SA accuracy. Recently, several studies have been proposed on ABSA (Schouten et al 2018 ; Alqaryouti et al 2019 ; Wang 2021 ; Kumar et al 2020 ; Nandal et al 2020 ; Li et al 2020 ; Prathi et al 2020 ; Alamanda 2020 ; Shams et al 2020 ; Bie and Yang 2021 ).…”

Section: Related Workmentioning

confidence: 99%

“…The novel approach of Cross-Lingual Sentiment Classification (CLSC) is proposed by Wang ( 2021 ) for sentiment classification. The aspect, opinion, and sentiment classification model has been designed using the unsupervised machine learning technique.…”

Section: Related Workmentioning

confidence: 99%

“…From the above studies, we observed that SA-based methods (Wang et al 2020 ; Hao et al 2020 ; Zhu et al 2021 ; Naresh and Krishna 2021 ; Singh et al 2021 ; Munuswamy et al 2021 ; Ayyub et al 2020 ; Oyebode et al 2020 ; Iqbal et al 2019 ; Khan et al 2020 ) are not sufficient to address the challenges related to the exact representation of feelings, emotions, and opinions from online reviews. For that purpose, the ABSA techniques (Schouten et al 2018 ; Alqaryouti et al 2019 ; Wang 2021 ; Kumar et al 2020 ; Nandal et al 2020 ; Li et al 2020 ; Prathi et al 2020 ; Alamanda 2020 ; Shams et al 2020 ; Bie and Yang 2021 ) that have been proposed recently are studied but they also have limited scope or limited investigations. Effective feature representation is a challenging task as the use of aspect-related features addresses the ambiguities/sarcasm but fails to handle the negations, emotions, opinions, etc.…”

Section: Related Workmentioning

confidence: 99%

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HAS: Hybrid Analysis of Sentiments for the perspective of customer review summarization

Kaur

Sharma

2022

J Ambient Intell Human Comput

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The reviews posted online by the end-users can help the business owners obtain a fair evaluation of their products/services and take the necessary steps. However, due to the large volume of online reviews being generated from time to time, it becomes challenging for business owners to track each review. The Customer Review Summarization (CRS) model that can present the summarized information and offer businesses with significant acumens to understand the reason behind customers' choices and behavior, would therefore be desirable. We propose the Hybrid Analysis of Sentiments (HAS) for the perspective of effective CRS in this paper. The HAS consists of steps like pre-processing, feature extraction, and review classification. The pre-processing phase removes the unwanted data from the text reviews using Natural Language Processing (NLP) based on different pre-processing functions. For efficient feature extraction, the hybrid mechanism consisting of aspect-related features and review-related features is proposed to build the unique feature vector for each customer review. Review classification is performed using different supervised classifiers like Support Vector Machine (SVM), Naïve Bayes, and Random Forest. The experimental results show that HAS efficiently performed the sentiment analysis and outperformed the existing state-of-the-art techniques with an F1 score of 92.2%.

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“…Therefore, considering the correlation of labels is beneficial to the accuracy of the algorithm and improves the hit rate of prediction. Literature [17] proposed that label correlation can improve the accuracy of multilabel classifiers. The more relevant the labels considered, the higher the complexity of the model.…”

Section: Related Workmentioning

confidence: 99%

Recognition of Psychological Characteristics of Students’ Behavior Based on Improved Machine Learning

2021

Journal of Sensors

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Contemporary classroom teaching requires the combination of students’ classroom behavior and their psychological activities and appropriately changes the teaching mode according to students’ psychological characteristics. This paper analyzes the traditional characteristic recognition algorithm, and after improving its deficiencies, an improved characteristic extraction algorithm is proposed, based on the actual situation of classroom learning. This new algorithm can effectively improve the students’ psychological feature prediction; with the support of this algorithm, a comprehensive analysis model with classroom behavior recognition and psychological feature recognition is constructed; also, the functional structure of the system is built up. Through experimental research, the model proposed in this paper is analyzed, and the experimental data has approved that the systemic model could play an important role in classroom teaching.

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Coarse Alignment of Topic and Sentiment: A Unified Model for Cross-Lingual Sentiment Classification

Cited by 25 publications

References 31 publications

SODA: Detecting COVID-19 in Chest X-Rays With Semi-Supervised Open Set Domain Adaptation

SODA: Detecting COVID-19 in Chest X-Rays With Semi-Supervised Open Set Domain Adaptation

HAS: Hybrid Analysis of Sentiments for the perspective of customer review summarization

Recognition of Psychological Characteristics of Students’ Behavior Based on Improved Machine Learning

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