Special Issue Article
A Model of Textual Emotion Mining From Text Document
Mining social emotions from text and more documents are assigned by social users with emotion labels like happiness, warmness, and amusement. Documents categorized based on emotions and it help for related document selection in online. It can collect document from social users and assign emotion for the words in that document and based on the preference level we can achieve emotion for the whole document. In the existing approach usually the document model is the bag-of-word and there is no relationship between the words. In proposed work documents collected from online and using emotion-topic model for emotion modeling. It first generates topic from document followed by affective terms and effectively identify emotion for the topic. In online news collections show that the model is not only effective in extracting the meaningful latent topics, but also significantly improves the performance of social emotion prediction.