Embedding Refinement Framework for Targeted Aspect-Based Sentiment Analysis

Liang, Bin; Yin, Rongdi; Liu, Baiyang; Gui, Lin; He, Yulan; Yang, Min; Xu, Ruifeng

doi:10.1109/taffc.2021.3071388

Cited by 16 publications

(7 citation statements)

References 64 publications

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“…Ma and Guo [8] introduced a Dense Concatenation Memory Network for ABSA, showcasing the advancement in memory network applications for sentiment analysis. 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.…”

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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“…However, these methods suffer from the problem of noise, where irrelevant visual and textual information introduces noise into the representation of aspects, further reducing recognition performance. Additionally, some recent works have intro- duced external knowledge, such as facial information [10], adjective-noun pairs [11], and image captions [12], [13], to align targets and images. The content of social media posts is often casual and informal.…”

Section: Introductionmentioning

confidence: 99%

Position Perceptive Multi-Hop Fusion Network for Multimodal Aspect-Based Sentiment Analysis

Fan,

Chen

2024

IEEE Access

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With the increasing prevalence of multimodal user-generated content on social media, Multimodal Aspect-Based Sentiment Analysis (MABSA) has garnered significant attention in recent years. MABSA aims to classify the sentiment polarity of aspects mentioned in textual content by leveraging both textual and visual modalities. Previous studies have primarily focused on using transformer-based models to fuse information from different modalities. However, the presence of irrelevant information in images and text related to specific aspects can negatively impact sentiment analysis results. Many studies still face challenges in handling the noise introduced during the multimodal fusion process. To address this issue, we propose a novel Position-Perceptive Multi-hop Fusion Network (PPMFN) in this paper. Our method includes a position-perceptive module and an aspect-guided multi-hop interactive attention fusion module, aimed at enhancing modal position perception and aspect-guided multi-hop interactions to filter out irrelevant noise information. Typically, parts closer to the target are more relevant. The position-perceptive module is used to capture positional features from both images and text. The aspect-guided fusion module leverages a multihop interactive attention network to merge multimodal features while eliminating irrelevant information from both images and text. Extensive experimental results demonstrate that our framework consistently surpasses strong baseline models across two public datasets.

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“…Aspect-Based Sentiment Analysis (ABSA) is a fine-grained sentiment analysis task that attracts significant attention due to its ability to offer detailed sentiment information, making it applicable to various scenarios. Many previous works on ABSA have focused on analyzing entities expressing user sentiment and their interrelationships from text, such as aspect terms, opinion terms, and sentiment polarities Liang et al 2023). Users often express opinions through multimodal posts with both text and images, rather than just text.…”

Section: Introductionmentioning

confidence: 99%

Multi-level textual-visual alignment and fusion network for multimodal aspect-based sentiment analysis

Li,

Ding,

Lin

et al. 2024

Artif Intell Rev

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Multimodal Aspect-Based Sentiment Analysis (MABSA) is an essential task in sentiment analysis that has garnered considerable attention in recent years. Typical approaches in MABSA often utilize cross-modal Transformers to capture interactions between textual and visual modalities. However, bridging the semantic gap between modalities spaces and addressing interference from irrelevant visual objects at different scales remains challenging. To tackle these limitations, we present the Multi-level Textual-Visual Alignment and Fusion Network (MTVAF) in this work, which incorporates three auxiliary tasks. Specifically, MTVAF first transforms multi-level image information into image descriptions, facial descriptions, and optical characters. These are then concatenated with the textual input to form a textual+visual input, facilitating comprehensive alignment between visual and textual modalities. Next, both inputs are fed into an integrated text model that incorporates relevant visual representations. Dynamic attention mechanisms are employed to generate visual prompts to control cross-modal fusion. Finally, we align the probability distributions of the textual input space and the textual+visual input space, effectively reducing noise introduced during the alignment process. Experimental results on two MABSA benchmark datasets demonstrate the effectiveness of the proposed MTVAF, showcasing its superior performance compared to state-of-the-art approaches. Our codes are available at https://github.com/MKMaS-GUET/MTVAF.

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Embedding Refinement Framework for Targeted Aspect-Based Sentiment Analysis

Cited by 16 publications

References 64 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

Position Perceptive Multi-Hop Fusion Network for Multimodal Aspect-Based Sentiment Analysis

Multi-level textual-visual alignment and fusion network for multimodal aspect-based sentiment analysis

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