CL-XABSA: Contrastive Learning for Cross-Lingual Aspect-Based Sentiment Analysis

Lin, Nankai; Fu, Yingwen; Lin, Xiaotian; Zhou, Dong; Yang, Aimin; Jiang, Shengyi

doi:10.1109/taslp.2023.3297964

Cited by 11 publications

(2 citation statements)

References 30 publications

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“…Differences based on pronunciation and meaning. This research is also guided by previous research which discussed similar matters, namely contrastive analysis of language phonology (Long, 2018;Wang, 1996;Yang, 2023;Guan, 2024). Some data was found when communicating in Indonesian language class.…”

Section: Discussionmentioning

confidence: 86%

Homographs Between Indonesian and Thai: Contrastive Analysis

Mukhlas,

Lestari,

Khoirunnisa

et al. 2024

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This research aims to describe the forms and expressions of understanding in Indonesian and Thai with a focus on homographs. The techniques used in this research are observation and note taking techniques. To achieve this goal, the listening method was used in data collection with note-taking techniques, namely observing, listening and recording vocabulary. Next, classify Indonesian vocabulary and Thai vocabulary. Next, to analyze Indonesian vocabulary and Thai vocabulary, this was done by comparing the similarities between the forms of expression and meaning of vocabulary in Indonesian with vocabulary in Thai and then classifying them. The data source was obtained through interactions between students in language learning in Thai students' classes and then compared with Indonesian. The research results show that there are two types of homographs, namely, the same meaning and pronunciation, and the same pronunciation and different meaning.

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Section: Discussionmentioning

confidence: 86%

Homographs Between Indonesian and Thai: Contrastive Analysis

Mukhlas,

Lestari,

Khoirunnisa

et al. 2024

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“…Zhang et al [5] focused on detecting dependency-related sentiment features, pointing out the significance of understanding linguistic dependencies for aspect-level sentiment classification. Lin et al [6] proposed a contrastive learning approach for cross-lingual ABSA, indicating the growing need for models that can perform sentiment analysis across different languages. In the realm of transfer learning, Jahanbin and Chahooki [10] utilized hybrid deep transfer learning models to analyze sentiments of Twitter influencers, a method that has shown promise in enhancing the adaptability of ABSA models to different domains.…”

Section: Literature Reviewmentioning

confidence: 99%

RMDEASD: Integrating Rule Mining and Deep Learning for Enhanced Aspect-Based Sentiment Analysis Across Diverse Domains

Sonali Ridhorkar

2024

jes

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The rapidly evolving landscape of Aspect-Based Sentiment Analysis (ABSA) in the realm of natural language processing necessitates innovative approaches to comprehend and interpret the intricate nature of sentiments expressed in textual data. Traditional ABSA methods have often struggled with the nuanced sentiments inherent in various textual sources, limited in their ability to adapt to domain-specific vernacular and context. This study introduces a novel approach that synergizes rule mining with advanced deep learning techniques, aiming to address these limitations and enhance the precision and contextual understanding in sentiment analysis. Our proposed model integrates rule-based systems with deep learning Transformers, a method recognized for its effectiveness in extracting structured, domain-specific insights. This integration results in a significant enhancement in the model's ability to capture nuanced sentiments, as demonstrated by an 8.5% increase in aspect-based sentiment analysis precision and an 8.3% improvement in accuracy over existing methods. The model employs a combination of techniques including Bi-LSTM, BiGRU-CRF with RNNs, and ontology mapping operations, forming a Hybrid Multi-Level Architecture that effectively captures both sequential data relationships and semantic context. Additionally, the model incorporates Cross-Domain Transfer Learning, utilizing BERT-based pre-trained models with added layers for contextual semantics, which has shown notable generalization capabilities across various domains. Furthermore, this study refines evaluation parameters, tailoring metrics such as domain-specific accuracy, recall, and precision to more accurately assess model performance in particular domains. This is especially pertinent in our analysis of Twitter datasets specific to industries like finance and healthcare. The inclusion of Temporal Dynamics and Aspect Summarization, using techniques like the VARMAx process, provides insights into the evolution of sentiments and aspects over time, an aspect crucial for longitudinal sentiment analysis. The comprehensive testing of our model on diverse spatial and temporal datasets reveals not only improved precision and accuracy in sentiment analysis but also a reduction in delay, demonstrating its efficiency and responsiveness. The study's findings indicate that our approach could significantly impact fields reliant on sentiment analysis, such as market analysis, public opinion research, and social media monitoring, providing a more nuanced and accurate understanding of consumer sentiments and trends. This paper's contribution lies in its innovative amalgamation of rule-based and deep learning techniques, tailored evaluation metrics, and its emphasis on temporal dynamics, setting a new precedent in the field of ABSA and opening avenues for further research and application in real-world scenarios.

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A survey on imbalanced learning: latest research, applications and future directions

Chen,

Yang,

et al. 2024

Artif Intell Rev

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Imbalanced learning constitutes one of the most formidable challenges within data mining and machine learning. Despite continuous research advancement over the past decades, learning from data with an imbalanced class distribution remains a compelling research area. Imbalanced class distributions commonly constrain the practical utility of machine learning and even deep learning models in tangible applications. Numerous recent studies have made substantial progress in the field of imbalanced learning, deepening our understanding of its nature while concurrently unearthing new challenges. Given the field’s rapid evolution, this paper aims to encapsulate the recent breakthroughs in imbalanced learning by providing an in-depth review of extant strategies to confront this issue. Unlike most surveys that primarily address classification tasks in machine learning, we also delve into techniques addressing regression tasks and facets of deep long-tail learning. Furthermore, we explore real-world applications of imbalanced learning, devising a broad spectrum of research applications from management science to engineering, and lastly, discuss newly-emerging issues and challenges necessitating further exploration in the realm of imbalanced learning.

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CL-XABSA: Contrastive Learning for Cross-Lingual Aspect-Based Sentiment Analysis

Cited by 11 publications

References 30 publications

Homographs Between Indonesian and Thai: Contrastive Analysis

Homographs Between Indonesian and Thai: Contrastive Analysis

RMDEASD: Integrating Rule Mining and Deep Learning for Enhanced Aspect-Based Sentiment Analysis Across Diverse Domains

A survey on imbalanced learning: latest research, applications and future directions

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