Supervised Opinion Mining of Social Network Data Using a Bag-of-Words Approach on the Cloud

2012 ◽  
pp. 299-309 ◽  
Author(s):  
S. Fouzia Sayeedunnissa ◽  
Adnan Rashid Hussain ◽  
Mohd Abdul Hameed
Keyword(s):  
Social Network ◽  
Opinion Mining ◽  
Network Data ◽  
Bag Of Words ◽  
Social Network Data

scholarly journals Questions and features of automated social networks monitoring with support of intelligent user messages analysis.

2014 ◽  
Vol 2014 (4) ◽  
pp. 146-152 ◽  
Author(s):  
Александр Подвесовский ◽  
Aleksandr Podvesovskiy ◽  
Дмитрий Будыльский ◽  
Dmitriy Budylskiy
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Software Implementation ◽  
Network Data ◽  
Research Directions ◽  
Social Network Data ◽  
Monitoring Model

An opinion mining monitoring model for social networks introduced. The model includes text mining processing over social network data and uses sentiment analysis approach in particular. Practical usage results of software implementation and its requirements described as well as further research directions.

Abstract #1249: Novel Use of Physician Social Network Data to Map the Gap in the Endocrinology Workforce

2015 ◽  
Vol 21 ◽  
pp. 301
Author(s):  
Armand Krikorian ◽  
Lily Peng ◽  
Zubair Ilyas ◽  
Joumana Chaiban
Keyword(s):  
Social Network ◽  
Network Data ◽  
Social Network Data

The Multilevel p2 Model

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 42-47 ◽  
Author(s):  
Bonne J. H. Zijlstra ◽  
Marijtje A. J. van Duijn ◽  
Tom A. B. Snijders
Keyword(s):  
Social Network ◽  
Network Data ◽  
Mcmc Algorithm ◽  
Independent Observation ◽  
Single Observation ◽  
Multiple Networks ◽  
Social Network Data ◽  
Multiple Observations ◽  
Practical Support ◽  
The Social

The p 2 model is a random effects model with covariates for the analysis of binary directed social network data coming from a single observation of a social network. Here, a multilevel variant of the p 2 model is proposed for the case of multiple observations of social networks, for example, in a sample of schools. The multilevel p 2 model defines an identical p 2 model for each independent observation of the social network, where parameters are allowed to vary across the multiple networks. The multilevel p 2 model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) algorithm that was implemented in free software for the statistical analysis of complete social network data, called StOCNET. The new model is illustrated with a study on the received practical support by Dutch high school pupils of different ethnic backgrounds.

Topic Modeling in Large Scale Social Network Data

2015 ◽  
Author(s):  
Aman Ahuja ◽  
Wei Wei ◽  
Kathleen M. Carley
Keyword(s):  
Social Network ◽  
Topic Modeling ◽  
Large Scale ◽  
Network Data ◽  
Social Network Data

scholarly journals Social network data of Swiss farmers related to agricultural climate change mitigation

Data in Brief ◽  
2021 ◽  
Vol 35 ◽  
pp. 106898
Author(s):  
Cordelia Sophie Kreft ◽  
Mario Angst ◽  
Robert Huber ◽  
Robert Finger
Keyword(s):  
Climate Change ◽  
Social Network ◽  
Climate Change Mitigation ◽  
Network Data ◽  
Social Network Data ◽  
Change Mitigation

scholarly journals Social Networks and Loneliness During the COVID-19 Pandemic

2021 ◽  
Vol 7 ◽  
pp. 237802312098525
Author(s):  
Balazs Kovacs ◽  
Nicholas Caplan ◽  
Samuel Grob ◽  
Marissa King
Keyword(s):  
Social Network ◽  
Social Isolation ◽  
Close Relationships ◽  
Network Size ◽  
Global Network ◽  
Network Data ◽  
Network Characteristics ◽  
Face To Face ◽  
Social Network Data ◽  
Social Network Size

We utilize longitudinal social network data collected pre–COVID-19 in June 2019 and compare them with data collected in the midst of COVID in June 2020. We find significant decreases in network density and global network size following a period of profound social isolation. While there is an overall increase in loneliness during this era, certain social network characteristics of individuals are associated with smaller increases in loneliness. Specifically, we find that people with fewer than five “very close” relationships report increases in loneliness. We further find that face-to-face interactions, as well as the duration and frequency of interactions with very close ties, are associated with smaller increases in loneliness during the pandemic. We also report on factors that do not moderate the effect of social isolation on perceived loneliness, such as gender, age, or overall social network size.

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