MASAD: A large-scale dataset for multimodal aspect-based sentiment analysis

Zhou, Jie; Zhao, Jiabao; Huang, Jimmy Xiangji; Hu, Qinmin; He, Liang

doi:10.1016/j.neucom.2021.05.040

Cited by 39 publications

(6 citation statements)

References 12 publications

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“…Regarding the MATE task, similar to information extraction, sequential annotation methods such as CRF and GNN (Zhang et al 2021) are employed for entity extraction. In contrast, the MASC task is typically approached as a sequence classification problem, and various neural network-based models, including Bidirectional Long Short-Term Memory (BiL-STM) (Zhou et al 2021), BERT (Khan and Fu 2021), have been proposed.…”

Section: Multimodal Aspect-based Sentiment Analysismentioning

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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Section: Multimodal Aspect-based Sentiment Analysismentioning

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

View full text Add to dashboard Cite

show abstract

“…One of the recent research methods for analyzing sentiments is presented in [16] where a large-scale dataset of text and images is provided for classifying the orientation of sentiments. The polarity prediction for text, image and multimodal is applied on different dataset domains and the average accuracy for each aspect is determined.…”

Section: Supervised Sentiment Analysismentioning

confidence: 99%

Enhanced Sentiment Analysis Algorithms for Multi-Weight Polarity Selection on Twitter Dataset

Mostafa¹

2023

Intelligent Automation &Amp; Soft Computing

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Sentiment analysis is based on the orientation of user attitudes and satisfaction towards services and subjects. Different methods and techniques have been introduced to analyze sentiments for obtaining high accuracy. The sentiment analysis accuracy depends mainly on supervised and unsupervised mechanisms. Supervised mechanisms are based on machine learning algorithms that achieve moderate or high accuracy but the manual annotation of data is considered a time-consuming process. In unsupervised mechanisms, a lexicon is constructed for storing polarity terms. The accuracy of analyzing data is considered moderate or low if the lexicon contains small terms. In addition, most research methodologies analyze datasets using only 3-weight polarity that can mainly affect the performance of the analysis process. Applying both methods for obtaining high accuracy and efficiency with low user intervention during the analysis process is considered a challenging process. This paper provides a comprehensive evaluation of polarity weights and mechanisms for recent sentiment analysis research. A semi-supervised framework is applied for processing data using both lexicon and machine learning algorithms. An interactive sentiment analysis algorithm is proposed for distributing multi-weight polarities on Arabic lexicons that contain high morphological and linguistic terms. An enhanced scaling algorithm is embedded in the multi-weight algorithm to assign recommended weight polarities automatically. The experimental results are conducted on two datasets to measure the overall accuracy of proposed algorithms that achieved high results when compared to machine learning algorithms.

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“…The cleaned data is transferred to the next task, and the data features in the text are extracted for training to get a better effect of the textual representation. Finally, the prediction is carried out via the classifier as a way of judging the user's affective tendency [17].…”

Section: Basic Process Of Sentiment Analysismentioning

confidence: 99%

Analysis of the multiple dimensions of ideological education in Marxist theory

Jiang

2023

Applied Mathematics and Nonlinear Sciences

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A model is utilized in this paper to analyze the textual emotions of ideology in Marxist theory. Long and short-term memory networks are chosen as the main method of text analysis to construct the main process of Marxist ideology education. Combined with the hybrid self-attention mechanism, the efficiency of extracting data features from the text was improved. The results show that the ALBERT-SABL-based sentiment analysis model is 86.9% accurate in extracting the sentiment of the ideology, and the F1 value is 87.6%. Compared with TextCnn, the accuracy has improved by 1.8%. Different schools have different levels of identification with Marx’s ideology, and under the identification dimension, the highest sentiment identification dimension among the eight school samples is School 2, with a dimension of 100. This study provides reference data in the ideological education of Marxist theory and promotes the development of Marx’s ideology.

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MASAD: A large-scale dataset for multimodal aspect-based sentiment analysis

Cited by 39 publications

References 12 publications

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

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

Enhanced Sentiment Analysis Algorithms for Multi-Weight Polarity Selection on Twitter Dataset

Analysis of the multiple dimensions of ideological education in Marxist theory

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