2P1-N-056 Position tracking estimation of two or more persons by probabilistic approach(Intelligent Space 2,Mega-Integration in Robotics and Mechatronics to Assist Our Daily Lives)

2005 ◽  
Vol 2005 (0) ◽  
pp. 190
Author(s):  
Akihiro Nishimura ◽  
Yasushi Hada ◽  
Kuniaki Kawabata ◽  
Hajime Asama
Keyword(s):  
Probabilistic Approach ◽  
Position Tracking ◽  
Daily Lives ◽  
Intelligent Space

scholarly journals Discovering Perceptions in Online Social Media: A Probabilistic Approach

2014 ◽  
Vol 24 (09) ◽  
pp. 1273-1299 ◽  
Author(s):  
Derek Doran ◽  
Swapna S. Gokhale ◽  
Aldo Dagnino
Keyword(s):  
Social Media ◽  
Public Transportation ◽  
Probabilistic Approach ◽  
Language Models ◽  
Daily Lives ◽  
Smoothing Methods ◽  
Online Social Media ◽  
Novel Approach ◽  
Political Actions ◽  
Broad Region

People across the world habitually turn to online social media to share their experiences, thoughts, ideas, and opinions as they go about their daily lives. These posts collectively contain a wealth of insights into how masses perceive their surroundings. Therefore, extracting people's perceptions from social media posts can provide valuable information about pertinent issues such as public transportation, emergency conditions, and even reactions to political actions or other activities. This paper proposes a novel approach to extract such perceptions from a corpus of social media posts originating from a given broad geographical region. The approach divides the broad region into a number of sub-regions, and trains language models over social media conversations within these sub-regions. Using Bayesian and geo-smoothing methods, the ensemble of language models can be queried with phrases embodying a perception. Discrete and continuous visualization methods represent the extent to which social media posts within the sub-regions express the query. The capabilities of the perception mining approach are illustrated using transportation-themed scenarios.

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