@inproceedings{7d3f85f3d25a4395b8922f56d96ca96c,
title = "Detecting communities by sentiment analysis of controversial topics",
abstract = "Controversial topics, particularly political topics, often provoke very different emotions among different communities. By detecting and analyzing communities formed around these controversial topics we can paint a picture of how polarized a country is and how these communities evolved over time. In this research, we made use of Internet data from Twitter, one of the most popular online social media sites, to identify a controversial topic of interest and the emotions expressed towards the topic. Communities were formed based on Twitter users{\textquoteright} sentiments towards the topic. In addition, the network structure of these communities was utilized to reveal those Twitter users that played important roles in their respective communities.",
keywords = "Sentiment analysis, Social network analysis, Topic modeling, Twitter",
author = "Kangwon Seo and Rong Pan and Aleksey Panasyuk",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 9th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2016 ; Conference date: 28-06-2016 Through 01-07-2016",
year = "2016",
doi = "10.1007/978-3-319-39931-7_20",
language = "English (US)",
isbn = "9783319399300",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "206--215",
editor = "Nathaniel Osgood and Xu, {Kevin S.} and David Reitter and Dongwon Lee",
booktitle = "Social, Cultural, and Behavioral Modeling - 9th International Conference, SBP-BRiMS 2016, Proceedings",
}