The dual-channel takeaway sentiment analysis model based on RoBERTa-WWM

Hao Zhou; Jin Hou; Tao Wu; Xuemei Li; Yan Chen; Guobin Mao; Huiyang Yin

doi:10.1117/12.3033800

13 June 2024 The dual-channel takeaway sentiment analysis model based on RoBERTa-WWM

Hao Zhou, Jin Hou, Tao Wu, Xuemei Li, Yan Chen, Guobin Mao, Huiyang Yin

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131801W (2024) https://doi.org/10.1117/12.3033800
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

To facilitate self-examination for businesses and enable government agencies to monitor food safety based on sentiment analysis of takeaway reviews, this paper proposes an RoBERTa-WWM-based sentiment analysis model for the takeaway industry, termed RLMT. The model converts the text expressions in takeaway reviews into dynamic semantic feature representations using the RoBERTa-WWM model. The study integrates the global semantic features extracted by combining BiLSTM (Bidirectional Long Short-Term Memory Network) with the Multi-head Attention mechanism and the local semantic features derived from TextCNN (Text Convolutional Network). The fused features are then processed through a fully connected layer and the Softmax function to obtain the final sentiment polarity. Experimental results illustrate that the RLMT model demonstrates superior sentiment classification performance on the waimai_10k dataset compared to current popular sentiment analysis methods, with precision, recall, and F1 values reaching 93.62%, 92.62%, and 92.62%, respectively.

Citation Download Citation

Hao Zhou, Jin Hou, Tao Wu, Xuemei Li, Yan Chen, Guobin Mao, and Huiyang Yin "The dual-channel takeaway sentiment analysis model based on RoBERTa-WWM", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131801W (13 June 2024); https://doi.org/10.1117/12.3033800

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available