A Study on Consideration and Tendency Analysis of Abnormal Motive Criminal

2021 ◽  
Vol 7 (2) ◽  
pp. 101-127
Author(s):  
Sang-Wom An ◽  
Keyword(s):  
Tendency Analysis

Polychronic tendency analysis: a new approach to understanding women's shopping behaviors

2004 ◽  
Vol 21 (5) ◽  
pp. 332-342 ◽  
Author(s):  
Jay D. Lindquist ◽  
Carol F. Kaufman‐Scarborough
Keyword(s):  
New Approach ◽  
Tendency Analysis

scholarly journals Anthropometry Among Non-sedentary Elderly Tendency Analysis Of Adiposity Over Three Decades

2019 ◽  
Vol 51 (Supplement) ◽  
pp. 915
Author(s):  
Jõao da Silva Junior ◽  
Rafael Benito Mancini ◽  
Carolina Gonzalez Beltran ◽  
Tatiane Kosimenko Ferrari ◽  
Timoteo Leandro Araujo ◽  
...  
Keyword(s):  
Tendency Analysis

scholarly journals A Multilayer Naïve Bayes Model for Analyzing User’s Retweeting Sentiment Tendency

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Mengmeng Wang ◽  
Wanli Zuo ◽  
Ying Wang
Keyword(s):  
Information Diffusion ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Structure Information ◽  
Bayes Model ◽  
Dynamic Social Network ◽  
Dynamic Social Networks ◽  
Text Information ◽  
Naïve Bayes Model ◽  
Tendency Analysis

Today microblogging has increasingly become a means of information diffusion via user’s retweeting behavior. Since retweeting content, as context information of microblogging, is an understanding of microblogging, hence, user’s retweeting sentiment tendency analysis has gradually become a hot research topic. Targeted at online microblogging, a dynamic social network, we investigate how to exploit dynamic retweeting sentiment features in retweeting sentiment tendency analysis. On the basis of time series of user’s network structure information and published text information, we first model dynamic retweeting sentiment features. Then we build Naïve Bayes models from profile-, relationship-, and emotion-based dimensions, respectively. Finally, we build a multilayer Naïve Bayes model based on multidimensional Naïve Bayes models to analyze user’s retweeting sentiment tendency towards a microblog. Experiments on real-world dataset demonstrate the effectiveness of the proposed framework. Further experiments are conducted to understand the importance of dynamic retweeting sentiment features and temporal information in retweeting sentiment tendency analysis. What is more, we provide a new train of thought for retweeting sentiment tendency analysis in dynamic social networks.

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