Method of Online Public Opinion Sentiment Recognition Based on Hybrid Machine Learning
[Objective/Significance]Improve the efficiency and accuracy of online public opinion sentiment recognition,and provide effective technical support for decision-makers to evaluate public opinion tendencies.[Methods/Processes]This study comprehensively utilizes the advantages of machine learning and deep learning,and organically combines multi-channel feature embedding methods such as Sentimet_Embedding and Word2Vec,deep neural network models such as BLSTM and CNN,as well as technical strategies such as random dropout and batch normalization,constructs a hybrid machine learning based online public opinion sentiment recognition model that integrates text sentiment polarity and pre trained semantic features.Finally,the feasibility and effectiveness of the model are verified by collecting social media public opinion data.[Limitations]The performance of the method and model has not yet reached its optimal level,and further improvements are needed in the future.[Results/Conclusions]Research results indicate that the performance of online public opinion sentiment recognition model can be significantly improved through multi-channel feature embedding methods and hybrid overlay neural networks.The online public opinion sentiment recognition model based on hybrid machine learning has higher accuracy than traditional machine learning classification models or single deep learning classification models.
Hybrid Machine LearningDeep LearningOnline Public OpinionSentiment RecognitionFeature Embedding
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