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

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%.