Sentence-level sentiment analysis via BERT and BiGRU

Shen, Jianghong; Liu, Xiaodong; Zhuang, Tao

doi:10.1117/12.2550215

Cited by 5 publications

(2 citation statements)

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“…Recently, models based on deep learning methods have made progress in this area. In 2019, Shen et al proposed a model which combines a Bidirectional Encoder Representation from Transformers (BERT) with BiGRU to gain the contextualized embeddings before performing sentiment analysis [14] . Some researchers also believe that the ensemble of deep learning methods and traditional feature-based methods could further improve the accuracy of sentiment classification.…”

Section: Related Workmentioning

confidence: 99%

NeedFull – a Tweet Analysis Platform to Study Human Needs During the COVID-19 Pandemic in New York State

2020

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Governments and municipalities need to understand their citizens' psychological needs in critical times and dangerous situations. COVID-19 brings lots of challenges to deal with. We propose NeedFull, an interactive and scalable tweet analysis platform, to help governments and municipalities to understand residents' real psychological needs during those periods. The platform mainly consists of four parts: data collection module, data storage module, data analysis module and data visualization module. The four parts interact with each other and provide users with a thorough human needs analysis based on their queries. We employed the proposed platform to investigate the reaction of people in New York State to the ongoing worldwide COVID-19 pandemic.

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Section: Related Workmentioning

confidence: 99%

NeedFull – a Tweet Analysis Platform to Study Human Needs During the COVID-19 Pandemic in New York State

2020

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“…(1) BiGRU [25]: Regardless of the contextual information in the conversation, it treats each utterance as an independent instance and uses a bidirectional GRU to encode the utterance and classify sentiment. (2) BERT [26]: This model is used to construct the utterance representations which are sent to a two-layer perceptron with a final SoftMax layer for sentiment classification.…”

Section: Baselines Methodsmentioning

confidence: 99%

Customer Sentiment Recognition in Conversation Based on Contextual Semantic and Affective Interaction Information

Huang

Liu

Zhu³

et al. 2023

Applied Sciences

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In the e-commerce environment, conversations between customers and businesses contain a multitude of useful information about customer sentiment. By mining that information, customer sentiment can be validly identified, which is helpful in accurately identifying customer needs and improving customer satisfaction. Contextual semantics information and inter-speaker affective interaction information are two key factors for identifying customers’ sentiments in conversation. Unfortunately, none of the existing approaches consider the two factors simultaneously. In this paper, we propose a conversational sentiment analysis method based on contextual semantic and affective interaction information. The proposed approach uses different bidirectional gated recurrent unit (BiGRU) combined with attention mechanisms to encode the contextual semantic information of different types of conversational texts. For modeling affective interactions, we use directed graph structures to portray the affective interactions between speakers and encode them with affective interaction features using graph convolutional neural networks (GCN). Finally, the two features are afused to recognize customer sentiment. The experimental results on the JDDC dataset show that our model can more accurately recognize customer sentiment than other baseline models in customer service conversation.

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UCM: Personalized Document-Level Sentiment Analysis Based on User Correlation Mining

Qiu

Luo

2023

Lecture Notes in Computer Science

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Sentence-level sentiment analysis via BERT and BiGRU

Cited by 5 publications

References 0 publications

NeedFull – a Tweet Analysis Platform to Study Human Needs During the COVID-19 Pandemic in New York State

NeedFull – a Tweet Analysis Platform to Study Human Needs During the COVID-19 Pandemic in New York State

Customer Sentiment Recognition in Conversation Based on Contextual Semantic and Affective Interaction Information

UCM: Personalized Document-Level Sentiment Analysis Based on User Correlation Mining

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