A Framework for Polarity Classification and Emotion Mining from Text (International Journal of Engineering and Computer Science, 2014)
Nowadays Online Social Networks are so popular that they are become a major component of an individual’s social interaction. They are also emotionally-rich environments where users share their emotions, feelings, ideas and thoughts. In this paper, a novel framework is proposed for characterizing emotional interactions in social networks. The aim is to extract the emotional content of texts in online social networks. The interest is in to determine whether the text is an expression of the writer’s emotions or not if yes then what type of emotion likes happy, sad, angry, disgust, fear, surprise. For this purpose, text mining techniques are performed on comments/messages from a social network. The framework provides a model for data collection, feature generation, data preprocessing and data mining steps. In general, the paper presents a new perspective for studying emotions’ expression in online social networks. The technique adopted is unsupervised; it mainly uses the k-means clustering algorithm and nearest neighbor algorithm. Experiments show high accuracy for the model in both determining subjectivity of texts and predicting emotions.