Article: Exploration and analysis of online public opinion detection in digital economy based on deep learning Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.7 pp.79 - 94 Abstract: With the booming development of digital economy, the internet has become an important platform for social emotions to converge. The detection of online public opinion is of vital importance to social stability. Focusing on the issue that current research cannot dynamically extract sentiment features, firstly, we use the dual-channel deep learning model to obtain the global and local sentiment features of the network comments respectively, enhance the sentiment features by the improved attention mechanism (EAM), and fuse the features to classify the sentiment of the public opinion using softmax. Subsequently, this paper classified the network opinion into levels and through the results of sentiment classification and combined with many indicators, a comprehensive calculation was carried out to obtain the public opinion detection level. Experimental outcome on two Twitter datasets show that the proposed method improves the weighted average F1 by 4.12-19.6%. Inderscience Publishers - linking academia, business and industry through research

Title: Exploration and analysis of online public opinion detection in digital economy based on deep learning

Authors: Min Qiu

Addresses: School of Economics and Management, Hebi School of Engineering and Technology, Henan University of Technology, Hebi 458000, China

Abstract: With the booming development of digital economy, the internet has become an important platform for social emotions to converge. The detection of online public opinion is of vital importance to social stability. Focusing on the issue that current research cannot dynamically extract sentiment features, firstly, we use the dual-channel deep learning model to obtain the global and local sentiment features of the network comments respectively, enhance the sentiment features by the improved attention mechanism (EAM), and fuse the features to classify the sentiment of the public opinion using softmax. Subsequently, this paper classified the network opinion into levels and through the results of sentiment classification and combined with many indicators, a comprehensive calculation was carried out to obtain the public opinion detection level. Experimental outcome on two Twitter datasets show that the proposed method improves the weighted average F1 by 4.12-19.6%.

Keywords: web public opinion detection; deep learning; attention mechanism; text sentiment categorisation; opinion rating calculation.

DOI: 10.1504/IJICT.2025.145702

International Journal of Information and Communication Technology, 2025 Vol.26 No.7, pp.79 - 94

Received: 10 Feb 2025
Accepted: 20 Feb 2025
Published online: 15 Apr 2025 *

Title: Exploration and analysis of online public opinion detection in digital economy based on deep learning

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