Detecting Dependency-Related Sentiment Features for Aspect-Level Sentiment Classification

Zhang, Xing; Xu, Jingyun; Cai, Yi; Tan, Xingwei; Zhu, Changxi

doi:10.1109/taffc.2021.3063259

Cited by 10 publications

(3 citation statements)

References 43 publications

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“…In order to ignore irrelevant information in the dependency tree, Guo et al (2019) proposed to assign weights based on the correlation between the nodes of the original dependency. For aspect‐level sentiment analysis, Zhang et al (2023) proposed weighted dependency to quantify the dependency between an aspect word and a sentiment word. In addition, several studies have focused on the significance of syntactic distance in text.…”

Section: Related Workmentioning

confidence: 99%

Linking negative herding effect to online public opinion: A new BiGADG approach for sentiment classification

Zhou,

Hu,

Liu

et al. 2024

Expert Systems

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The negative herding effect tends to cause the spread of negative emotions among netizens, resulting in significant public opinion crises. Therefore, linking the negative herding effect and online public opinion, we aim to exploring the connection between netizens' emotions, opinions, and the negative herding effect. To better distinguish the emotions in texts, a new BiGADG (Bidirectional Gated recurrent unit based on Attention with Distance‐based dependency syntax Graph convolutional network) model is proposed in this article. This model includes primarily the proposed dependency syntax based on dependency distance, Bidirectional Gated Recurrent Unit (BiGRU), multi‐head self‐attention and Graph Convolutional Network (GCN). Particularly, the dependency syntax based on dependency distances incorporates word‐to‐word dependency distances into the traditional dependency syntax, which enriches the syntactic structural features of the text. After the experiments, the accuracy of BiGADG model is 86.53%, which is 3.11% higher compared to BERT model. It is evident that our model for sentiment classification has been improved. Based on the results of sentiment classification, we investigate the existence of negative herding behaviour and its underlying motivation in online public opinion. We have found that the negative herding effect, if not effectively managed, may trigger large‐scale and uncontrollable public opinion crises.

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Section: Related Workmentioning

confidence: 99%

Linking negative herding effect to online public opinion: A new BiGADG approach for sentiment classification

Zhou,

Hu,

Liu

et al. 2024

Expert Systems

View full text Add to dashboard Cite

show abstract

“…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.…”

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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“…Moreover, GRU is used for long-term dependency learning and CNN is used for the generation of local features. However, this model faces the issue of dependency learning which is enhanced in [17], it introduced dependency-related phenomena to identify the dependency-related feature for given aspect term; dependency parse tree is designed and dependency-related feature is integrated into BiLSTM and CNN. Furthermore, this research work observed that sentiment features of a given aspect help in discriminating the sentiment polarity.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis and Classification Using Deep Semantic Information and Contextual Knowledge

Al-Absi¹,

Kang²,

Al-Absi³

2023

Computers, Materials &Amp; Continua

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Sentiment analysis (AS) is one of the basic research directions in natural language processing (NLP), it is widely adopted for news, product review, and politics. Aspect-based sentiment analysis (ABSA) aims at identifying the sentiment polarity of a given target context, previous existing model of sentiment analysis possesses the issue of the insufficient exaction of features which results in low accuracy. Hence this research work develops a deep-semantic and contextual knowledge networks (DSCNet). DSCNet tends to exploit the semantic and contextual knowledge to understand the context and enhance the accuracy based on given aspects. At first temporal relationships are established then deep semantic knowledge and contextual knowledge are introduced. Further, a deep integration layer is introduced to measure the importance of features for efficient extraction of different dimensions. Novelty of DSCNet model lies in introducing the deep contextual. DSCNet is evaluated on three datasets i.e., Restaurant, Laptop, and Twitter dataset considering different deep learning (DL) metrics like precision, recall, accuracy, and Macro-F1 score. Also, comparative analysis is carried out with different baseline methods in terms of accuracy and Macro-F1 score. DSCNet achieves 92.59% of accuracy on restaurant dataset, 86.99% of accuracy on laptop dataset and 78.76% of accuracy on Twitter dataset.

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Detecting Dependency-Related Sentiment Features for Aspect-Level Sentiment Classification

Cited by 10 publications

References 43 publications

Linking negative herding effect to online public opinion: A new BiGADG approach for sentiment classification

Linking negative herding effect to online public opinion: A new BiGADG approach for sentiment classification

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

Sentiment Analysis and Classification Using Deep Semantic Information and Contextual Knowledge

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