Chinese Text Sentiment Analysis Based on BI-GRU and Self-attention

Pan, Yaxing; Liang, Mingfeng

doi:10.1109/itnec48623.2020.9084784

Cited by 20 publications

(7 citation statements)

References 4 publications

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“…The BiGRU-Attention neural network model proposed in [20]. c. The BiLSTM-Attentions model proposed in [21]. This model combines BiLSTM with two layers of Attention.…”

Section: Comparison Experiments and Parameter Settingsmentioning

confidence: 99%

Retracted: Software based sentiment analysis of clinical data for healthcare sector

Vimal¹,

Albuquerque

Barbosa

et al. 2023

IET Software

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Aiming at the simple neural network's lack of the ability to capture the contextual semantics of text and extract meaningful information in the text, a sentiment analysis model FAGB iAGRU is a fusion of attention mechanism and gated recurrent unit (GRU), is designed. First, the text is preprocessed, and passed GloVe carries out word vectorisation to reduce the dimension of the vector space; then, the attention mechanism is merged with the update gate of the gating unit to form a hybrid model so that it can extract meaningful information in the text features; finally, by forcing forward attention The mechanism further removes text features, and then classifies them by the softmax classifier. Experiments on public data sets show that the algorithm can effectively improve the performance of sentiment analysis. K E Y W O R D Sbelief networks, client-server systems, contracts, data models, data privacy, data protectionThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…The BiGRU-Attention neural network model proposed in [20]. c. The BiLSTM-Attentions model proposed in [21]. This model combines BiLSTM with two layers of Attention.…”

Section: Comparison Experiments and Parameter Settingsmentioning

confidence: 99%

Retracted: Software based sentiment analysis of clinical data for healthcare sector

Vimal¹,

Albuquerque

Barbosa

et al. 2023

IET Software

View full text Add to dashboard Cite

show abstract

“…To incorporate contextual information, RNNs introduce an in-memory unit to retain a slice of crucial features so that long-distance dependencies between tokens can be captured. The classical modified type of RNNs, long short-term memory [ 36 ], and gated recurrent units [ 37 ], are currently the most popular networks used for text sentiment analysis. In addition, a sequential model with an attention mechanism has been effectively proved in sentiment analysis [ 38 ].…”

Section: Related Workmentioning

confidence: 99%

A Lightweight Sentiment Analysis Framework for a Micro-Intelligent Terminal

Wei

Wang

et al. 2023

Sensors

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Sentiment analysis aims to mine polarity features in the text, which can empower intelligent terminals to recognize opinions and further enhance interaction capabilities with customers. Considerable progress has been made using recurrent neural networks or pre-trained models to learn semantic representations. However, recently published models with complex structures require increasing computational resources to reach state-of-the-art (SOTA) performance. It is still a significant challenge to deploy these models to run on micro-intelligent terminals with limited computing power and memory. This paper proposes a lightweight and efficient framework based on hybrid multi-grained embedding on sentiment analysis (MC-GGRU). The gated recurrent unit model is designed to incorporate a global attention structure that allows contextual representations to be learned from unstructured text using word tokens. In addition, a multi-grained feature layer can further enrich sentence representation features with implicit semantics from characters. Through hybrid multi-grained representation, MC-GGRU achieves high inference performance with a shallow structure. The experimental results of five public datasets show that our method achieves SOTA for sentiment classification with a trade-off between accuracy and speed.

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“…(3) Atten-BiGRU [16]: combining the attention mechanism with BiGRU to learn text features more precisely according to the context and extract deep semantic features of the text, with less number of parameters and shorter training time compared to LSTM.…”

Section: Benchmark Experimentsmentioning

confidence: 99%

A multi-scale sarcasm sentiment recognition algorithm incorporating sentence hierarchical representation

Zhang

Zheng

et al. 2023

Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)

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Sarcasm is a special kind of linguistic sentiment that is widely used in a wide range of social media to express strong emotions in users. Therefore, the task of sarcasm recognition is particularly important for social media analysis. There are few studies on sarcasm sentiment recognition in Chinese, and they often ignore the complex interactions between different syntactic components of a sentence, such as sentiment words, entities, dummy words, and special punctuation that occur in the text. In order to improve the accuracy of Chinese sarcasm recognition, this paper proposes a multi-scale neural network sarcasm recognition algorithm incorporating a hierarchical representation of sentences, taking into account the semantic information of sentences and the relationship features between different syntactic components. The hierarchical syntactic tree is reconstructed to distinguish the key components of the sentence, and the multi-channel convolutional network is used to mine the relational features between syntactic levels and deeply fuse them with semantic information to perform the Chinese sarcastic sentiment recognition task. We have tested the method on a publicly available Chinese sarcastic comment dataset, and the results show that the method can effectively improve the accuracy rate of Chinese sarcastic sentiment recognition.

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Chinese Text Sentiment Analysis Based on BI-GRU and Self-attention

Cited by 20 publications

References 4 publications

Retracted: Software based sentiment analysis of clinical data for healthcare sector

Retracted: Software based sentiment analysis of clinical data for healthcare sector

A Lightweight Sentiment Analysis Framework for a Micro-Intelligent Terminal

A multi-scale sarcasm sentiment recognition algorithm incorporating sentence hierarchical representation

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