Aspect Level Sentiment Analysis with Aspect Attention

Li, Changliang; Wang, Hailiang; He, Saike

doi:10.1007/978-3-030-26142-9_22

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…DFLGA-BERT [17] ：OF adds quasi-attention to the global feature extractor and uses subtractive attention to attenuate noisy information in text when fusing attention.…”

Section: Contrast Methodsmentioning

confidence: 99%

Bilingual multi-feature sentiment analysis based on relationship degree

Ding

Zhang

2023

International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023)

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Aiming at the problem that the monolingual corpus (Chinese or English) is affected by the sentence structure in the sentiment analysis task, resulting in a large deviation of the extracted context information, and the traditional model does not consider the dependence between aspect words and context information words, which makes the sentiment classification inaccurate, a bilingual multi-feature sentiment analysis model with the degree of fusion relationship is proposed. Firstly, the attention mechanism is used to calculate the attention relationship degree of the aspect word in the sentence, and the Hadama product operation is performed with the context information to obtain the dependent information of the aspect word. Then, the Bidirectional Gated Recurrent Unit (BiGRU) is used to learn the global serialization information of the sentence to obtain the overall information characteristics of the sentence. Next, through the attention mechanism, the overall information characteristics and the aspect word-dependent features are integrated to obtain single-language information. Finally, through the attention mechanism, the overall information characteristics and the aspect word-dependent features are integrated to obtain single-language information. Experimental results on the public datasets Sem Eval2014 Task4 and AI Challenger 2018 show that the model can obtain sentiment information based on aspect words more comprehensively, and has higher accuracy of text sentiment polarity classification than the comparison model.

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“…DFLGA-BERT [17] ：OF adds quasi-attention to the global feature extractor and uses subtractive attention to attenuate noisy information in text when fusing attention.…”

Section: Contrast Methodsmentioning

confidence: 99%

Bilingual multi-feature sentiment analysis based on relationship degree

Ding

Zhang

2023

International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023)

View full text Add to dashboard Cite

show abstract

“…The same author also expands on the concept of sentiment of an opinion by expressing that the former can be represented as a triple that contains a sentiment type (which depends on the application of the analysis), a sentiment polarity (positive, negative, or even neutral), and intensity (which indicates the strength of the opinion expressed). There are four levels of SA mentioned across the surveyed literature, depending on the level of specificity at which the analysis is applied: document level [41], sentence level [42], aspect level [43], and concept level SA [44]. These are all briefly described as follows:…”

Section: Sentiment Analysis: Fundsmentioning

confidence: 99%