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Application of deep learning fusion algorithm in natural language processing in emotional semantic analysis
Author(s) -
Gong Yunlu,
Lu Nannan,
Zhang Jiajian
Publication year - 2018
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4779
Subject(s) - computer science , key (lock) , artificial intelligence , process (computing) , sentiment analysis , support vector machine , natural language processing , emotional intelligence , semantic analysis (machine learning) , semantics (computer science) , semantic computing , information fusion , deep learning , machine learning , semantic web , psychology , computer security , social psychology , programming language , operating system
Summary With the development of network technology, people are facing more and more massive information. How to extract emotional information in massive information rapidly has received more and more attention from people. This paper introduces the principle and structure of the traditional emotional model. Different personality, emotional states, and external stimuli will have different effects on emotional semantic analysis. In addition, this paper has proposed emotional semantic analysis method based on wake‐sleep and SVM method. The model starts from the description and calculation of the dynamic characteristics of emotions and more fully predicts the process characteristics that describe the evolution of emotions. Search and category browsing allows users to quickly access these information points. In addition, this paper provides a deep learning fusion algorithm in emotional semantic analysis, introduces its reference implementation and related key technologies, and supports business intelligence to a certain extent, and it has a strong application prospect on the network data information.