Aspect-level sentiment analysis merged with knowledge graph and graph convolutional neural network

Dai, Zuhua; Liu, Yuanyuan; Di, Shilong; Fan, Qi

doi:10.1088/1742-6596/2083/4/042044

Cited by 5 publications

(4 citation statements)

References 3 publications

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“…For aspect-based sentiment analysis, researchers often use deep learning graph convolutional neural networks (GCN). For instance, Dai Zuhua et al [2] used GCNs to construct semantically enhanced aspect-level text sentiment models. Chunxia Yang et al [3] proposed incorporating tight connections into GCN to capture local and global information.…”

Section: Related Workmentioning

confidence: 99%

Graph Convolutional Networks for Aspect-Based Sentiment Analysis

2024

jaip

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Aspect-based sentiment analysis, as an important fine-grained sentiment analysis problem, aims to analyze and understand the emotions at the aspect level in sentences. However, existing models often overlook the syntactic relationship between words and fail to extract specific semantic information.We propose a graph convolutional network model to extract aspectual word features from local context. This paper also proposes a model to address the problem of under-use of explicit syntactic dependency in aspect category emotion analysis, based on a graph convolutional network and incorporating external knowledge to extract deep and surface structure information from the dependency graph, using the aspect item as a reference point. Both models are superior to existing models.

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Section: Related Workmentioning

confidence: 99%

Graph Convolutional Networks for Aspect-Based Sentiment Analysis

2024

jaip

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“…More than a decade later, this topic is still controversial as no exact conclusion was accepted by the academic community (Liu et al 2021;Skoric et al 2020).…”

Section: Politicsmentioning

confidence: 99%

“…Other challenges include polarity disambiguation (Cambria et al 2022), filtering neutral opinions and ambivalent opinions (opinions with both positive and negative sentiments) since these opinions can influence the overall perception of the sentiment (Chan et al 2023;Rahmani et al 2023). These issues have been addressed by new frameworks and techniques primarily based on neural networks and graph architectures (Cambria et al 2022;Chan et al 2023;Dai et al 2021;Rahmani et al 2023).…”

Section: Supervised Learningmentioning

confidence: 99%

Social media discourse and voting decisions influence: sentiment analysis in tweets during an electoral period

Rita

António

Afonso

2023

Soc. Netw. Anal. Min.

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In a time where social media is fundamental for any political campaign and to share a message with an electoral audience, this study searches for a conclusion of the actual persuasion capacity of social media in the electors when they need to decide whom to vote for as their next government. For this, it compares the sentiment that Social Media users demonstrated during an electoral period with the actual results of those elections. For this analysis, it was used, as a case study, tweets mentioning the two major English parties, Conservative and Labor, their respective candidates for the position of prime minister, and terms that identified their political campaign during the electoral period of the General Elections of the United Kingdom that occurred on December 12, 2019. Data were collected using R. The treatment and analysis were done with R and RapidMiner. Results show that tweets’ sentiment is not a reliable election results predictor. Additionally, results also show that it is impossible to state that social media impacts voting decisions. At least not from the polarity of the sentiment of opinions on social media.

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“…3. Because aspect-based sentiment analysis belongs to fine-grained sentiment analysis, many existing multimodal sentiment analysis methods lack the ability to solve such problems (Dai et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Image and Text Aspect Level Multimodal Sentiment Classification Model Using Transformer and Multilayer Attention Interaction

Yin,

Chen

2023

International Journal of Data Warehousing and Mining

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Many existing image and text sentiment analysis methods only consider the interaction between image and text modalities, while ignoring the inconsistency and correlation of image and text data, to address this issue, an image and text aspect level multimodal sentiment analysis model using transformer and multi-layer attention interaction is proposed. Firstly, ResNet50 is used to extract image features, and RoBERTa-BiLSTM is used to extract text and aspect level features. Then, through the aspect direct interaction mechanism and deep attention interaction mechanism, multi-level fusion of aspect information and graphic information is carried out to remove text and images unrelated to the given aspect. The emotional representations of text data, image data, and aspect type sentiments are concatenated, fused, and fully connected. Finally, the designed sentiment classifier is used to achieve sentiment analysis in terms of images and texts. This effectively has improved the performance of sentiment discrimination in terms of graphics and text.

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Aspect-level sentiment analysis merged with knowledge graph and graph convolutional neural network

Cited by 5 publications

References 3 publications

Graph Convolutional Networks for Aspect-Based Sentiment Analysis

Graph Convolutional Networks for Aspect-Based Sentiment Analysis

Social media discourse and voting decisions influence: sentiment analysis in tweets during an electoral period

Image and Text Aspect Level Multimodal Sentiment Classification Model Using Transformer and Multilayer Attention Interaction

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