A Framework for Polarity Classification and Emotion Mining from Text

No wadays Online Social Networks are so popular that they are become a major component of an individual's social interaction. They are also emotionally-rich environ ments 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 exp ression 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 d ata collection, feature generation, data preprocessing and data min ing steps. In general, the paper presents a new perspective fo r studying emotions' expression in online social networks. The technique adopted is unsupervised; it main ly uses the k-means clustering algorithm and nearest neighbor algorithm. Experiments show high accuracy for the model in both determin ing subjectivity of texts and predicting e motions.

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