Abstract
Network interaction has evolved into a grouping paradigm as civilization has progressed and artificial intelligence technology has advanced. This network group model has quickly extended communication space, improved communication content, and tailored to the demands of netizens. The fast growth of the network community on campus can assist students in meeting a variety of communication needs and serve as a vital platform for their studies and daily lives. It is investigated how to extract opinion material from comment text. A strategy for extracting opinion attitude words and network opinion characteristic words from a single comment text is offered at a finer level. The development of a semiautonomous domain emotion dictionary generating technique improves the accuracy of opinion and attitude word extraction. This paper proposes a window-constrained Latent Dirichlet Allocation (LDA) topic model that improves the accuracy of extracting network opinion feature words and ensures that network opinion feature words and opinion attitude words are synchronized by using the location information of opinion attitude words. The two-stage opinion leader mining approach and the linear threshold model based on user roles are the subjects of model simulation tests in this study. It is demonstrated that the two-stage opinion leader mining method suggested in this study can greatly reduce the running time while properly finding opinion leaders with stronger leadership by comparing the results with existing models. It also shows that the linear threshold model based on user roles proposed in this paper can effectively limit the total number of active users who are activated multiple times during the information diffusion process by distinguishing the effects of different user roles on the information diffusion process.
Highlights
With the continuous advancement of Internet technology, people’s communication methods on the Internet have undergone significant changes, from searching for information, reading news, and watching videos to gathering and discussing in groups such as Weibo, QQ, and WeChat [1]
This paper proposes a method of synchronously extracting the opinion attitude words of a single comment text and the network opinion characteristic words at a fine-grained level
A window-constrained Latent Dirichlet Allocation (LDA) topic model is developed, which employs the position information of opinion attitude words to increase the accuracy of network opinion feature word extraction and guarantees that network opinion feature words and opinion attitude words are synchronized
Summary
With the continuous advancement of Internet technology, people’s communication methods on the Internet have undergone significant changes, from searching for information, reading news, and watching videos to gathering and discussing in groups such as Weibo, QQ, and WeChat [1] This type of network group disrupts traditional face-to-face gatherings and discussions, allowing everyone to share information in a virtual Internet space, resulting in a diverse public space of discourse and a network of action organizations, which has become the interpersonal and emotional connection of college students. College students are fast thinkers and receptive to new ideas They like expressing their thoughts on national politics, social hotspots, and what they have seen and heard in the Internet community, but they lack reasonable thought and judgement, lack education and experience, and have inadequate perspectives on subjects. The linear threshold model described in this research is both rational and effective
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