Studies on a multidimensional public opinion network model and its topic detection algorithm

2019 ◽  
Vol 56 (3) ◽  
pp. 584-608 ◽  
Author(s):  
Guanghui Wang ◽  
Yuxue Chi ◽  
Yijun Liu ◽  
Yufei Wang
Keyword(s):  
Public Opinion ◽  
Network Model ◽  
Detection Algorithm ◽  
Topic Detection

Hierarchy Topic Detection and Hot Topic Identification

2014 ◽  
Vol 701-702 ◽  
pp. 180-186
Author(s):  
Xue Mei Zhou ◽  
Shan Ying Cheng
Keyword(s):  
Public Opinion ◽  
Real Time ◽  
Hierarchical Structure ◽  
Clustering Algorithm ◽  
Detection Algorithm ◽  
Topic Detection ◽  
Topic Identification ◽  
Evaluation Indexes ◽  
Detection Algorithms ◽  
The Hierarchical Structure

Due to the problem that the existing topic detection algorithms can not satisfy accuracy,real time and topic hierarchical clustering at the same time, this article builds a hierarchy topic detection algorithm based on improved single pass clustering algorithm. In addition, using public opinion evaluation indexes to analyze topic temperature,the method proposed in this paper can detect hot topics accurately and timely while showing the hierarchical structure of the topic .

G-GERT network model of online public opinion reversal based on kernel and grey degree

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shuli Yan ◽  
Xiangyan Zeng ◽  
Pingping Xiong ◽  
Na Zhang
Keyword(s):  
Public Opinion ◽  
Network Model ◽  
Design Methodology ◽  
Average Frequency ◽  
Decision Makers ◽  
Content Type ◽  
Gert Network ◽  
Frequency Probability ◽  
And Control ◽  
Difficult Issue

PurposeIn recent years, online public opinion reversal incidents have been occurring frequently, which has increased the complexity of the evolution of online public opinion, and they have become a difficult issue for public opinion management and control. It is of great significance to explore the regularity of online public opinion reversal.Design/methodology/approachCombined with the grey characteristics of online public opinion information, a grey graphical evaluation review technique (G-GERT) network model is constructed based on kernel and grey degree, and the frequency, probability and time of online public opinion reversal nodes are calculated using C-marking method and Z-marking method.FindingsThroughout the online public opinion reversal events, there are all repeated outbreak nodes occurring, so the authors regard the repeated occurrence of outbreak nodes as reversal. According to the average frequency, probability and time of repeated outbreak nodes in the G-GERT network model, the authors predict the corresponding key information of reversal. It can simulate the evolution process of public opinion events accurately.Originality/valueThe G-GERT network model based on kernel and grey degree reveals the regulation of public opinion reversal, predicts the frequency, probability and time of reversal nodes, which are the most concerned and difficult issues for decision-makers. The model provides the decision basis and reference for government decision-making departments.

Multi-level topic detection algorithm for Netnews Specials

2013 ◽  
Author(s):  
Yu Peng ◽  
ZhiQing Lin ◽  
Bo Xiao ◽  
Chuang Zhang
Keyword(s):  
Detection Algorithm ◽  
Topic Detection ◽  
Multi Level

The Blog Topic Detection Research Based on Bursty Word

2014 ◽  
Vol 926-930 ◽  
pp. 3406-3409
Author(s):  
Tao Kuang ◽  
Shan Hong Zhu
Keyword(s):  
Similarity Measure ◽  
Clustering Analysis ◽  
Main Idea ◽  
Design Strategy ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Topic Detection ◽  
Media Report ◽  
Word Similarity ◽  
Detecting Method

The emergence of blog hot topic means that the user's interest ,participation behavior and various media report coverage reach to its climax,a detecting method of topics on blog based on blog bursty words is proposed. It includes the use of word similarity measure and text clustering analysis which is combined with design strategy in specific period, the use of the main idea of the sudden vocabulary hot topic detection algorithm has to be used and improved in order to generate the final clustering. The experimental results show that the algorithm can obtain an accurate blog topic detection results.

scholarly journals Public Opinion Analysis of Road Accidents & Review of Conventional and Combinational Driver Safety Methodologies with SelfLearning Algorithms

2021 ◽  
Vol 12 (2) ◽  
pp. 3245-3260
Author(s):  
Tamilarasi A, Et. al.
Keyword(s):  
Public Opinion ◽  
Tamil Nadu ◽  
Detection System ◽  
Detection Algorithm ◽  
Driver Assistance ◽  
Accurate Analysis ◽  
Fitting Algorithm ◽  
Tamil Nadu State ◽  
Curvature Estimation ◽  
Accident Detection

Advanced driver assistance and accident detection system is significantlyneeded to ensure safety for drivers. Drowsiness detection, collision detection and various driver alert systems have penetrated into market with an aim to provide higher security for driver but due to population of vehicle and modification in structure of roads these system fails to answer safety problems that results in severe accidents.In this paper we provide accurate analysis of past recorded accidents in Tamil Nadu state and analysis of public opinion on Accident detection system is carried out using 1004 licensed persons under different ages in three cities (Coimbatore, Erode and Nilgiris) by focusing the major 10 parameters carrying 48 Questions. Findings and implications of this analysis is also discussed in this article. A thorough analysis of recent techniques that are used for AAD (Automatic Accident Detection) and road safety programmes that resolve the pre and post cautionary concerns of accidents in developing countries is addressed with the review of most 4 influencing algorithms in ITS for AAD.. 1 Vehicle Detection using Wheel arc Counter Detection Algorithm, 2 Enhancement of V2X Communication using Multi-RAT,3Road Curvature Estimation using Circle Fitting Algorithm and4Driver Safety Systemis discussed in this paper. To understand recent computational challenges and extended areas of research in ITS, anhybrid approach of CNN with VANETs for accident detection has been suggested to enumerate the obtained accidental information.

scholarly journals Image target detection algorithm compression and pruning based on neural network

2021 ◽  
Vol 18 (2) ◽  
pp. 499-516
Author(s):  
Yan Sun ◽  
Zheping Yan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Target Detection ◽  
Network Model ◽  
Detection Algorithm ◽  
Image Feature ◽  
Propagation Mode ◽  
Learning Technology ◽  
Image Detection ◽  
Detection Algorithms

The main purpose of target detection is to identify and locate targets from still images or video sequences. It is one of the key tasks in the field of computer vision. With the continuous breakthrough of deep machine learning technology, especially the convolutional neural network model shows strong Ability to extract image feature in the field of digital image processing. Although the model research of target detection based on convolutional neural network is developing rapidly, but there are still some problems in practical applications. For example, a large number of parameters requires high storage and computational costs in detected model. Therefore, this paper optimizes and compresses some algorithms by using early image detection algorithms and image detection algorithms based on convolutional neural networks. After training and learning, there will appear forward propagation mode in the application of CNN network model, providing the model for image feature extraction, integration processing and feature mapping. The use of back propagation makes the CNN network model have the ability to optimize learning and compressed algorithm. Then research discuss the Faster-RCNN algorithm and the YOLO algorithm. Aiming at the problem of the candidate frame is not significant which extracted in the Faster- RCNN algorithm, a target detection model based on the Significant area recommendation network is proposed. The weight of the feature map is calculated by the model, which enhances the saliency of the feature and reduces the background interference. Experiments show that the image detection algorithm based on compressed neural network image has certain feasibility.

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