Lifecycle-Based Event Detection from Microblogs

Mu, Lin; Jin, Peiquan; Zheng, Lizhou; Chen, Enhong; Yue, Lihua

doi:10.1145/3184558.3186338

Cited by 12 publications

(5 citation statements)

References 27 publications

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“…Scholars raise various methods to divide the network public sentiment lifecycle [ 9 , 10 ]. The main difference is the late-stage (decline and termination stage).…”

Section: Related Workmentioning

confidence: 99%

The Public Sentiment Analysis of Double Reduction Policy on Weibo Platform

Jia

Peng

2022

Computational Intelligence and Neuroscience

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Weibo platform is an indispensable transmission channel in education policy release and dissemination. The events and sentiments contained in education policies microblogs include the public sentiment and support the general management and guidance scientifically and efficiently. This study constructs a dataset based on the “Double Reduction Policy” relevant microblogs and comments. The policy events are extracted by Latent Dirichlet Allocation (LDA) model and Language Technology Platform (LTP). Based on the emotion dictionary, an attention-based BiLSTM model is constructed to classify the public sentiments. The experimental results reveal four themes: “industry impact,” “institutional supervision,” “public feedback,” and “policy implementation.” The distribution conforms to the development trend of online public sentiments.

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“…Scholars raise various methods to divide the network public sentiment lifecycle [ 9 , 10 ]. The main difference is the late-stage (decline and termination stage).…”

Section: Related Workmentioning

confidence: 99%

The Public Sentiment Analysis of Double Reduction Policy on Weibo Platform

Jia

Peng

2022

Computational Intelligence and Neuroscience

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“…Scientific research on the evolution trend of microblog events can effectively monitor the development of event popularity at all stages [4][5][6], which is of great significance to the supervision of network opinion. At present, existing work can be divided into the following two aspects: event propagation research and event trend prediction research.…”

Section: Related Workmentioning

confidence: 99%

Trend Prediction of Event Popularity from Microblogs

Zhao

2021

Future Internet

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Owing to rapid development of the Internet and the rise of the big data era, microblog has become the main means for people to spread and obtain information. If people can accurately predict the development trend of a microblog event, it will be of great significance for the government to carry out public relations activities on network event supervision and guide the development of microblog event reasonably for network crisis. This paper presents effective solutions to deal with trend prediction of microblog events’ popularity. Firstly, by selecting the influence factors and quantifying the weight of each factor with an information entropy algorithm, the microblog event popularity is modeled. Secondly, the singular spectrum analysis is carried out to decompose and reconstruct the time series of the popularity of microblog event. Then, the box chart method is used to divide the popularity of microblog event into various trend spaces. In addition, this paper exploits the Bi-LSTM model to deal with trend prediction with a sequence to label model. Finally, the comparative experimental analysis is carried out on two real data sets crawled from Sina Weibo platform. Compared to three comparative methods, the experimental results show that our proposal improves F1-score by up to 39%.

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“…Mu et al (2018) implemented a system that efficiently detects 5W1H (Who, When, Where, What, Why, and How) as events from microblogging. The system performs step‐by‐step sentiment analysis for events, making it possible to know the change in people's emotional tendencies toward the event.…”

Section: Related Workmentioning

confidence: 99%

Measurement and quantification of an individual's feelings for a place in personal data analysis

Kasuya

Tago

Jin

2021

Human Behav and Emerg Tech

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Location‐related data are an important subset of personal data. An individual may have a positive or negative feeling for a specific place, which is important for personal data analysis. There are many studies on sentiment analysis within text data, such as tweets, but few studies have been conducted specifically on an individual's feelings regarding locations. In this study, we focus on measuring and quantifying an individual's feelings for a place using three representative methods in sentiment analysis: emotion dictionary, personalized dictionary, and Bayesian classification. We design an experiment to evaluate these methods using tweet data including locations and an individual's emotional changes with regard to these locations before entering, after exiting, and in a location. Three sets of emotion scores are obtained and normalized. Furthermore, we set four protocols and use statistical methods to compare these emotion scores with the subjective emotion scores provided by the user whose tweets are used in the experiment. Experimental results show that Bayesian classification performs the best in measuring and quantifying an individual's feelings for a place.

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Lifecycle-Based Event Detection from Microblogs

Cited by 12 publications

References 27 publications

The Public Sentiment Analysis of Double Reduction Policy on Weibo Platform

The Public Sentiment Analysis of Double Reduction Policy on Weibo Platform

Trend Prediction of Event Popularity from Microblogs

Measurement and quantification of an individual's feelings for a place in personal data analysis

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