Analyzing patterns of user content generation in online social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Relational learning via latent social dimensions
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Characterizing user behavior in online social networks
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Toward Predicting Collective Behavior via Social Dimension Extraction
IEEE Intelligent Systems
Understanding latent interactions in online social networks
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Sentiment knowledge discovery in twitter streaming data
DS'10 Proceedings of the 13th international conference on Discovery science
Discovering Overlapping Groups in Social Media
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Enhanced sentiment learning using Twitter hashtags and smileys
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Target-dependent Twitter sentiment classification
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
User-level sentiment analysis incorporating social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
The influence of personality on Facebook usage, wall postings, and regret
Computers in Human Behavior
How user behavior is related to social affinity
Proceedings of the fifth ACM international conference on Web search and data mining
Using content and interactions for discovering communities in social networks
Proceedings of the 21st international conference on World Wide Web
Scalable Learning of Collective Behavior
IEEE Transactions on Knowledge and Data Engineering
Real-World behavior analysis through a social media lens
SBP'12 Proceedings of the 5th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Circle-based recommendation in online social networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
| Hi-index | 0.00 |
Social scientists have identified several network relationships and dimensions that induce homophily. Sentiments or opinions towards different issues have been observed as a key dimension which characterizes human behavior. Twitter is an online social medium where rapid communication takes place publicly. People usually express their sentiments towards various issues. Different persons from different walks of social life may share same opinion towards various issues. When these persons constitute a group, such groups can be conveniently termed same wavelength groups. We propose a novel framework based on sentiments to identify such same wavelength groups from twitter domain. The analysis of such groups would be of help in unraveling their response patterns and behavioral features.