Abstract
Text makes up a large portion of network data because it is the vehicle for people's direct expression of emotions and opinions. How to analyze and mine these emotional text data has become a hot topic of concern in academia and industry in recent years. The online LDA (Latent Dirichlet Allocation) model is used in this paper to train the social hot topic data of professional migrant workers on the same time slice, and the subtopic evolution and intensity are obtained. The topic development is divided into four categories, and the classification model is created using SVM (Support Vector Machine). Instead of decision makers, a virtual human with sensibility and rationality is built using a hierarchical emotional cognitive model to solve multiobjective optimization problems interactively. It analyzes human body structure and emotional signals, and then combines them with visual and physiological signals to create multimodal emotional data. An example is used to demonstrate the effectiveness of the proposed model.
Highlights
Entrepreneurship of professional migrant workers can effectively alleviate social employment pressure, narrow the income gap between urban and rural areas, and promote new urbanization
Knowing the information trends of social events on the Weibo platform in real time, as well as tracking and predicting the social hot topics of professional migrant workers on a continuous basis, allows the government and enterprises to grasp public opinion trends in real time and guide public opinion, which is of great social importance to both the government and businesses
E development of the topic is divided into four categories. e classification model is built using SVM, and the data between two peaks is predicted using a short-term prediction model. e experimental results show that this method’s topic popularity classification accuracy is 88 percent
Summary
Entrepreneurship of professional migrant workers can effectively alleviate social employment pressure, narrow the income gap between urban and rural areas, and promote new urbanization. Ese energies are positive and negative, while events with negative energy are more likely to cause largescale discussions At this time, local problems may become public topics in the country, causing huge social panic, and sometimes even requiring government intervention [2, 3]. E monitoring of professional migrant workers’ social hot-spot public opinion requires the use of Weibo’s hot-spot detection. Because the emotional content of the Computational Intelligence and Neuroscience text is treated to other content in the theme modeling process, and the semantic strength of the emotional theme is determined by the proportion of emotional words in the text, it is still insufficient in highlighting the emotional semantics [5, 6]. Traditional theme modeling ignores semantic relationship patterns such as text sequence and word context, instead treating the text as a word bag and determining the theme solely based on the word co-occurrence relationship, which limits the text representation ability
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