Predictive Model for Internet Public Opinion

Zeng, Jianping; Zhang, Shiyong; Wu, Chengrong; Xie, Jianfeng

doi:10.1109/fskd.2007.453

Cited by 21 publications

(10 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, Peter Jorgensen [12] explores the use of an interactive activation with competition (IAC) artificial neural network (ANN) to find relationships in email texts. PJianping Zeng [5] introduced Hidden Markov Model (HMM) to describe the activity of IPO. G.M.…”

Section: Related Workmentioning

confidence: 99%

Internet Public Opinion Hotspot Detection and Analysis Based on Kmeans and SVM Algorithm

Hong

2010

2010 International Conference of Information Science and Management Engineering

View full text Add to dashboard Cite

Rapid progress of network arouses much attention on Internet public opinion, it is important to grasp the internet public opinion in time and understand the trends of their opinion correctly. Text mining plays a fundamental role in categorization and monitoring of internet public opinion, but internet public opinion is much more difficult than pure-text process because of their semi-structured characteristic. To address this issue, we propose a model for internet public opinion hotspot detection and analysis. Due to the text format of internet public opinion, we introduce the traditional vector space model (VSM) to express them, and then use Kmeans algorithm to perform text clustering on a corpus collected from some news website, and use SVM classifier to perform text categorization for new text opinion analysis, the result of the experiment shows that the efficiency and effectiveness of such method.

show abstract

Section: Related Workmentioning

confidence: 99%

Internet Public Opinion Hotspot Detection and Analysis Based on Kmeans and SVM Algorithm

Hong

2010

2010 International Conference of Information Science and Management Engineering

View full text Add to dashboard Cite

show abstract

“…Nevertheless, the different orders of these three types of words and the default language habits of people may bring about various analyzing results, like "HEN BU HAO" (meaning "very bad" in Chinese)" and "BU SHI HEN HAO" (meaning "not very good" in Chinese). In this case, the same word meat-information is used in these two phrases, but due to the different orders of words, they express a totally different meaning, at least on the degree of sentiment [13].…”

Section: B Public Opinion Preprocesmentioning

confidence: 99%

A Public Opinion Analysis System for Urban Management Information

Chen

Li²,

Shen³

et al. 2014

JCP

View full text Add to dashboard Cite

Digital urban management has been a fast developing trend in modern cities. And as the development of the urban management, there are an increasing number of people finding that public opinion information is quite valuable and important for this process. So in this paper, we design a public opinion analysis system for urban management information (POASUMI), in which we implement certain functionalities like public opinion information acquisition, Chinese word segmentation, analysis and statistic of sentiment and hotspot of public opinion information. The experimental results indicate that the results generated by POASUMI can match actual condition very well, definitely providing quite valuable information for urban management.

show abstract

“…log( ) t e (2) Step 4, continuously generate null output in the time interval of t 0 +1 and t 0 + t , i.e.,…”

Section: Input: Parameter and Simulation Time Length Tmentioning

confidence: 99%

“…We have used hidden Markov model (HMM) to describe the number of postings, and verify that it is suitable for describing the dynamic [2]. In a short range dependence model, we use HMM as a generative model.…”

Section: Short Range Dependence Modelmentioning

confidence: 99%

“…A life profile modeling based on the sequence of chronological outcomes is introduced and hidden Markov model (HMM) is used to model the activeness of events in websites [1]. A new HMM with adaptive selection on the number of hidden state is employed to model the topic activities in interactive websites, and prediction experiments on several topics show that the modeling is efficient [2]. However, the feasibility of applying HMM to a whole website, which usually contains large amount of topics, is still doubtful.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Approach to model WEB activity based on phase transition theory

Zeng

Zhang

2008

2008 3rd International Conference on Intelligent System and Knowledge Engineering

View full text Add to dashboard Cite

Many kinds of interactive WEB spaces have attracted more and more people to express their own opinion, and communicate with others, etc. However, the complexity that is generated from topics, users' activity, and their evolution properties leads to the difficulty in describing the dynamic in WEB activities. Considering a WEB space as an open system which locates at a far from equilibrium state, we apply phase transition theory to capture the macroscopic dynamic in WEB activity and proposed a generative model. A Hurst sequence which identifies the macroscopic property like the temporal dependency and phase transition is defined. A set of Hurst sequences which is extracted from a WEB space is used to compute its dynamic characteristic. Related algorithms, such as FHS generating algorithm are provided. Experiments on FUDAN BBS analysis are done and the results show that the proposed method can identify the WEB dynamic well.

show abstract

Predictive Model for Internet Public Opinion

Cited by 21 publications

References 7 publications

Internet Public Opinion Hotspot Detection and Analysis Based on Kmeans and SVM Algorithm

Internet Public Opinion Hotspot Detection and Analysis Based on Kmeans and SVM Algorithm

A Public Opinion Analysis System for Urban Management Information

Approach to model WEB activity based on phase transition theory

Contact Info

Product

Resources

About