Cross-Domain Aspect-Based Sentiment Classification by Exploiting Domain- Invariant Semantic-Primary Feature

Zhang, Bowen; Fu, Xianghua; Luo, Chuyao; Ye, Yunming; Li, Xutao; Jing, Liwen

doi:10.1109/taffc.2023.3239540

Cited by 9 publications

(3 citation statements)

References 58 publications

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“…According to Zhang et al [17], transformer-based semantic-primary knowledge transferring (TSPKT) network for aspect-term sentiment analysis across domains makes use of semantic-primary information to facilitate the transfer of knowledge across multiple domains. Before semantic-primary information is collected from the S-Graph, it is generated using external semantic lexicons.…”

Section: Related Workmentioning

confidence: 99%

Aspect based sentiment analysis using a novel ensemble deep network

Rajan,

Manur

2024

IJ-AI

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Aspect-based sentiment analysis (ABSA) is a fine-grained task in natural language processing, which aims to predict the sentiment polarity of several parts of a sentence or document. The essential aspect of sentiment polarity and global context have deep relationships that have not received enough attention. This research work design and develops a novel ensemble deep network (EDN) which comprises the various network and integrated to enhance the model performance. In the proposed work the words of the input sentence are converted into word vectors using the optimised bidirectional encoder representations from transformers (BERT) model and an optimised BERT-graph neural networks (GNN) model with convolutions is built that analyses the ABSA of the input sentence. The optimised GNN model with convolutions for context-based word representations is developed for the word-vector embedding. We propose a novel EDN for an ABSA model for optimised BERT over GNN with convolutions. The proposed ensemble deep network proposed system (EDN-PS) is evaluated with various existing techniques and results are plotted in terms of metrics for accuracy and F1- score, concluding that the proposed EDN-PS ensures better performance in comparison with the existing model.

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Section: Related Workmentioning

confidence: 99%

Aspect based sentiment analysis using a novel ensemble deep network

Rajan,

Manur

2024

IJ-AI

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“…Liang et al [9] developed an embedding refinement framework targeted for ABSA, furthering the understanding of how sophisticated embedding techniques can enhance sentiment analysis accuracy. In addressing the challenges of cross-domain analysis, Zhang et al [13] exploited domain-invariant semantic-primary features for cross-domain ABSA, highlighting the importance of domain adaptability in sentiment analysis models. Cao et al [15] implemented a heterogeneous reinforcement learning network with external knowledge for ABSA, demonstrating the integration of external knowledge sources for improving model performance.…”

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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“…in which LN and FF are the normalization and feed-forward layers of the BERT model. Zhang et al (2023a) developed the Transformer-based Semantic-Primary Knowledge Transferring network model (TSPKT) considering syntactic relationships and generalizing semantic relations. They argue that high-level abstractions of aspects and opinion terms help in cross-domain scenarios.…”

Section: Rulesmentioning

confidence: 99%

Systematic Review on Aspect-Based SentimentAnalysis in Cross-Domain

Santin,

Rezende

2024

Preprint

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Aspect-level sentiment analysis is crucial for consumers and institutions, enabling them to monitor satisfaction regarding specific aspects of products and services through user reviews. Over time, various artificial intelligence techniques have been implemented with significant success. However, most of these techniques rely heavily on a substantial amount of labeled data. In this context, Cross-Domain Aspect-Based Sentiment Analysis (ABSA) emerges, leveraging data from source domains to enhance performance in the target domain. This systematic review contributes to this framework by outlining the primary solutions developed to tackle this challenge. It presents their data sources, compared methods, and the evolution of the main technologies adopted while identifying gaps that may inspire future research endeavors. A new classification of models is proposed here, considering the cross-domain approach. This fresh perspective aims to assist researchers in their quest for innovation, clarifying the context of their proposal and suggesting relevant comparisons with existing works.

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Cross-Domain Aspect-Based Sentiment Classification by Exploiting Domain- Invariant Semantic-Primary Feature

Cited by 9 publications

References 58 publications

Aspect based sentiment analysis using a novel ensemble deep network

Aspect based sentiment analysis using a novel ensemble deep network

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

Systematic Review on Aspect-Based SentimentAnalysis in Cross-Domain

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