Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments
Framework for building intelligent mobile social applications
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Emotion detection state of the art
Proceedings of the CUBE International Information Technology Conference
Automated Twitter data collecting tool for data mining in social network
Proceedings of the 2012 ACM Research in Applied Computation Symposium
Automated Twitter data collecting tool and case study with rule-based analysis
Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services
Emotion-based character clustering for managing story-based contents: a cinemetric analysis
Multimedia Tools and Applications
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Online Social Networks are so popular nowadays that they are a major component of an individual’s social interaction. They are also emotionally-rich environments where close friends share their emotions, feelings and thoughts. In this paper, a new framework is proposed for characterizing emotional interactions in social networks, and then using these characteristics to distinguish friends from acquaintances. The goal is to extract the emotional content of texts in online social networks. The interest is in whether the text is an expression of the writer’s emotions or not. For this purpose, text mining techniques are performed on comments retrieved from a social network. The framework includes a model for data collection, database schemas, data processing and data mining steps. The informal language of online social networks is a main point to consider before performing any text mining techniques. This is why the framework includes the development of special lexicons. In general, the paper presents a new perspective for studying friendship relations and emotions’ expression in online social networks where it deals with the nature of these sites and the nature of the language used. It considers Lebanese Face book users as a case study. The technique adopted is unsupervised, it mainly uses the k-means clustering algorithm. Experiments show high accuracy for the model in both determining subjectivity of texts and predicting friendship.