Fine-Grained Sentiment Analysis of Cross-Domain Chinese E-Commerce Texts Based on SKEP_Gram-CDNN

Zhang, Yanrong; Zhu, Chengxiang; Xie, Yisong

doi:10.1109/access.2023.3296447

Cited by 2 publications

(1 citation statement)

References 20 publications

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“…Cryptocurrency sentiment analysis, as investigated by Girsang and Stanley [33], and the impact of annotators' selection by Gadi and Sicilia [35], illustrate the diverse applications and considerations in ABSA. 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.…”

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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A Fine-Grained Sentiment Analysis Method Using Transformer for Weibo Comment Text

Xue,

Bai

2024

International Journal of Information Technologies and Systems Approach

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Many existing fine-grained sentiment analysis (FGSA) methods have problems such as easy loss of fine-grained information, difficulty in solving polysemy and imbalanced sample categories. Therefore, a Transformer based FGSA method for Weibo comment text is proposed. Firstly, the RoBERTa model with knowledge augmentation was used to dynamically encode the text so as to solving the polysemy issue. Then, BiLSTM is used to effectively capture bidirectional global semantic dependency features. Next, Transformer is used to fuse multi-dimensional features and adaptively strengthen key features to overcome the problem of fine-grained information loss. Finally, an improved Focal Loss function is utilized for training to solve the issue of imbalanced sample categories. As demonstrated by the experimental outcomes on the SMP2020-EWECT, NLPCC 2013 Task 2, NLPCC 2014 Task 1, and weibo_senti_100k datasets, the suggested method outperforms the alternatives for advanced comparison methods.

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Fine-Grained Sentiment Analysis of Cross-Domain Chinese E-Commerce Texts Based on SKEP_Gram-CDNN

Cited by 2 publications

References 20 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

A Fine-Grained Sentiment Analysis Method Using Transformer for Weibo Comment Text

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