An explainable sentiment prediction model based on the portraits of users sharing representative opinions in social sensors

Wang, Bin; Wang, Enhui; Zhu, Zikun; Sun, Yangyang; Tao, Yingjie; Wang, Wei

doi:10.1177/15501477211033765

Cited by 7 publications

(4 citation statements)

References 58 publications

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“…Users can fully express their emotions through emotive words on the web platform. It allows them to fully express themselves [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Forwarding Volume of Campus Microblog Public Opinion Emergencies Using Neural Network

2022

Mobile Information Systems

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With the continuous expansion of colleges and universities, the proportion of college students among netizens has increased rapidly. They have become an important component of Chinese netizens and the main audience of Weibo. It is easier for online public opinion to form in some social media platforms than in others. We explain places for political conversation in Chinese cyberspace in terms of interaction, which results in various types of political discussion, based on research on authoritarian deliberation. The crisis of college network public opinion caused by the improper use of Weibo by college students occurs frequently. College network public opinion has become an important factor affecting the development and stability of colleges and universities. Social network rumor forwarding behavior refers to whether users forward specific rumors. Also, there is no surety about the truthfulness of the rumor. Taking Weibo as an example, this paper proposes a neural network-based model for predicting the forwarding volume of public opinion emergencies on campus Weibo. It solves the problem of low prediction accuracy of traditional SVM and other models. The data-driven experimental findings suggest that the technique described in this study can increase the accuracy of predicting forwarding volume in unexpected campus public opinion events.

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“…Users can fully express their emotions through emotive words on the web platform. It allows them to fully express themselves [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Forwarding Volume of Campus Microblog Public Opinion Emergencies Using Neural Network

2022

Mobile Information Systems

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“…e second is that the cultural brand "has a license but no market," and the market adaptability is not strong. e third is that the market segmentation is not enough, and the homogenization competition of regional cultural brands is serious [2]. e fourth is the lack of strong policy support for the development of cultural brands.…”

Section: Introductionmentioning

confidence: 99%

The Influence of Brand Visual Communication on Consumer Psychology Based on Deep Learning

Wang

2022

Mathematical Problems in Engineering

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In order to solve the problem that there are few methods of users’ consumption psychology, the author proposes a research on the influence of brand visual communication on consumer psychology based on deep learning. First, establish the mapping relationship between experience level-product features-aspect words and then use aspect word extraction technology, mining users’ attention to different experience levels from user comments and dividing users into three types: instinctive preference, behavioral preference, and reflective preference; finally, the deep learning-based aspect sentiment analysis technology is used to calculate the user’s preference for the product and further analyze the characteristics of different types of users. Experimental results show that based on the application analysis of more than 900,000 JD.com mobile phone review data, three types of consumer preference user groups were obtained, of which instinctive preference users accounted for 41.6%; it is higher than behavioral preference users (33.01%) and reflection preference users (25.39%), and the consumption characteristics of the three types of users are analyzed from the aspects of mobile phone brand and price. It is proved that the author’s user portrait method can better express the consumption preferences of different types of users.

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“…The maximum entropy model does not need to consider whether each feature value is related, nor does it require independent feature assumptions. However, based on attribute vectors, different attributes are randomly selected according to their differences [18]. Therefore, when studying multiple categories, the maximum entropy model is the most effective.…”

Section: Classification Of Movie Reviews Based On Sentiment Sentence ...mentioning

confidence: 99%

User Sentiment Analysis Methods for Elderly Social Media Networks

Shen

2023

SCPE

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For the analysis of user sentiment in social media networks for the elderly population, emotional sentences are first extracted to classify movie reviews. Afterwards, social network data of the elderly population based on user search behavior is analyzed. The movie reviews of elderly social media users are analyzed for rating prediction. The research results indicate that the accuracy of sentiment classification results is in descending order of Dirichlet, maximum entropy, and support vector machine. The highest classification accuracy of the three algorithms is 87.1%, 86.9%, and 86.5%, respectively. The classification accuracy of the first level classifiers of Dirichlet, maximum entropy, and support vector machine are 90.7%, 88.7%, and 87.4%, respectively. The classification accuracy of the second level classifier is 86.7%, 83.7%, and 80.4%, respectively. The predictive analysis results of the research method are superior to those generated by using Slope One. The method proposed in the study can promote emotional analysis of film review texts, improving the analysis accuracy.

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An explainable sentiment prediction model based on the portraits of users sharing representative opinions in social sensors

Cited by 7 publications

References 58 publications

Prediction of the Forwarding Volume of Campus Microblog Public Opinion Emergencies Using Neural Network

Prediction of the Forwarding Volume of Campus Microblog Public Opinion Emergencies Using Neural Network

The Influence of Brand Visual Communication on Consumer Psychology Based on Deep Learning

User Sentiment Analysis Methods for Elderly Social Media Networks

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