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   Articles with "aspect level" as a keyword



Joint sentence and aspect-level sentiment analysis of product comments

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recommendations! Published in 2021 at "Annals of Operations Research"

DOI: 10.1007/s10479-020-03534-7

Abstract: Comments from social media platforms (such as YouTube) have become a valuable resource for manufacturers to examine public opinion toward their products. Accordingly, we propose a novel framework for automatically collecting, filtering, and analyzing comments… read more here.

Keywords: sentence aspect; aspect level; sentiment analysis;

Interactive POS-aware network for aspect-level sentiment classification

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recommendations! Published in 2021 at "Neurocomputing"

DOI: 10.1016/j.neucom.2020.08.013

Abstract: Abstract Existing aspect-level sentiment-classification models completely rely on the learning from given datasets. However, these are easily misled by biased samples, resulting in learning some ill-suited rules that limit their potential. The information of some… read more here.

Keywords: information; pos; sentiment; aspect level ... See more keywords

CE-HEAT: An Aspect-Level Sentiment Classification Approach With Collaborative Extraction Hierarchical Attention Network

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recommendations! Published in 2019 at "IEEE Access"

DOI: 10.1109/access.2019.2954590

Abstract: Aspect-level sentiment classification is a fine-grained sentiment analysis task. It aims to predict the sentiment of a review in different aspects. Recently many works exploit the hierarchical attention network to capture the aspect-specific sentiment information.… read more here.

Keywords: hierarchical attention; sentiment; attention; level sentiment ... See more keywords

MulGCN: MultiGraph Convolutional Network for Aspect-Level Sentiment Analysis

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recommendations! Published in 2025 at "IEEE Access"

DOI: 10.1109/access.2025.3537340

Abstract: Aspect-level sentiment analysis (ALSA) is used to identify the sentiment polarities of the given aspects in a sentence. Various approaches have been proposed to improve the performance of ALSA, most recently graph convolutional networks (GCNs).… read more here.

Keywords: mulgcn; level; sentiment analysis; aspect level ... See more keywords

A Multimodal Aspect-Level Sentiment Analysis Model Based on Syntactic-Semantic Perception

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recommendations! Published in 2025 at "IEEE Access"

DOI: 10.1109/access.2025.3624546

Abstract: Current multimodal aspect-level sentiment analysis models often face two primary challenges. Firstly, there is insufficient integration of syntactic and semantic information during syntactic analysis. Secondly, there is excessive noise during the fusion of different modalities.… read more here.

Keywords: analysis; level sentiment; model; multimodal aspect ... See more keywords

MAN: Mutual Attention Neural Networks Model for Aspect-Level Sentiment Classification in SIoT

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recommendations! Published in 2020 at "IEEE Internet of Things Journal"

DOI: 10.1109/jiot.2020.2963927

Abstract: Text sentiment classification is of critical importance to improve the autonomous decision making and communication ability among object peers in the Social Internet of Things (SIoT). To classify sentiment polarity on a fine-grained level, aspect-level… read more here.

Keywords: sentiment classification; sentiment; level sentiment; attention ... See more keywords
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GSMNet: Global Semantic Memory Network for Aspect-Level Sentiment Classification

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recommendations! Published in 2021 at "IEEE Intelligent Systems"

DOI: 10.1109/mis.2020.3042253

Abstract: Aspect-level sentiment classification determines the sentiment polarity of a targeted aspect. To solve this task, attention-based neural networks are typically adopted to explore the interaction between the aspect and its context in a single sentence.… read more here.

Keywords: gsmnet; global semantic; aspect level; semantic memory ... See more keywords

LALDM: A Multimodal Aspect Level Text Analysis Method and Its Application in Online Consumer Electronics

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recommendations! Published in 2024 at "IEEE Transactions on Consumer Electronics"

DOI: 10.1109/tce.2024.3456792

Abstract: Aspect term extraction and aspect level sentiment analysis are key tasks. Although in the multimodal field, performance is enhanced by placing these two tasks in a unified framework, there is still room for improvement in… read more here.

Keywords: text; consumer electronics; analysis; language ... See more keywords
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Deep Learning Model for Interpretability and Explainability of Aspect-Level Sentiment Analysis Based on Social Media

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recommendations! Published in 2025 at "IEEE Transactions on Computational Social Systems"

DOI: 10.1109/tcss.2023.3347664

Abstract: The interactive attention graph convolution network (IAGCN), a novel model proposed in this article, will revolutionize aspect-level sentiment analysis (SA). IAGCN effectively addresses these key features, in contrast to prior research that ignored the meaning… read more here.

Keywords: level sentiment; model; sentiment analysis; aspect level ... See more keywords

Chinese text dual attention network for aspect-level sentiment classification

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recommendations! Published in 2024 at "PLOS ONE"

DOI: 10.1371/journal.pone.0295331

Abstract: English text has a clear and compact subject structure, which makes it easy to find dependency relationships between words. However, Chinese text often conveys information using situational settings, which results in loose sentence structures, and… read more here.

Keywords: chinese text; level; level sentiment; sentiment ... See more keywords

Incorporating Word Significance into Aspect-Level Sentiment Analysis

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recommendations! Published in 2019 at "Applied Sciences"

DOI: 10.3390/app9173522

Abstract: Aspect-level sentiment analysis has drawn growing attention in recent years, with higher performance achieved through the attention mechanism. Despite this, previous research does not consider some human psychological evidence relating to language interpretation. This results… read more here.

Keywords: word; aspect level; sentiment analysis; level sentiment ... See more keywords