Enhancing Arabic Aspect-Based Sentiment Analysis Using End-to-End Model

Shafiq, Ghada M.; Hamza, Taher; Alrahmawy, Mohammed F.; El-Deeb, Reem

doi:10.1109/access.2023.3342755

Cited by 2 publications

(1 citation statement)

References 44 publications

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“…Zhang et al [34] and Zhou et al [36] expanded on the theme of domain-specific and fine-grained sentiment analysis, with a focus on e-commerce texts and topic-enhanced language models, respectively. Shafiq et al [37] focused on enhancing Arabic ABSA using an end-to-end model, addressing the need for language-specific sentiment analysis solutions. This study highlights the importance of developing ABSA models that are adaptable to various languages and linguistic nuances.…”

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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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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Evaluating the Effect of Emotion Models on the Generalizability of Text Emotion Detection Systems

de León Languré,

Zareei

2024

IEEE Access

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Text emotion detection is a pivotal aspect of natural language processing, with wideranging applications involving human-computer interactions. Machine learning models have been trained with supervised methods, thus relying on labeled datasets. However, the arbitrary selection of emotion models while labeling such datasets poses significant challenges in the performance and generalizability of the produced machine learning models, primarily when evaluated against unseen data, as it effectively introduces bias to the process. This study investigates the impact of emotion model selection on the efficacy of machine learning models for text emotion detection. Eight labeled datasets were employed to train linear regression, feedforward neural network, and BERT-based deep learning models. Results demonstrated a notable decrease in accuracy when models trained on one dataset were tested on others, underscoring the inherent incompatibilities in labeling across datasets. To prove that the emotion model significantly impacts models' performance, we propose a standardized emotion label mapping utilizing James Russell's Circumplex Model of Affect, that turns the emotion model into a parameter rather than a fixed element. Cross-dataset testing with this shared emotion mapping yielded significant, non-negligible changes in accuracy (both improvement and degradation). This fact highlights the impact of the emotion model (traditionally arbitrarily selected) during machine learning training and performance, arguing that improvements in accuracy reported in related research literature might be due to differences in the used emotion model rather than the new algorithms introduced.

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Enhancing Arabic Aspect-Based Sentiment Analysis Using End-to-End Model

Cited by 2 publications

References 44 publications

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

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

Evaluating the Effect of Emotion Models on the Generalizability of Text Emotion Detection Systems

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