Implementation of Emotion Generation and Summarization form Affective Text

Emotion Generation and Summarization form Affective Text deals with new aspect for categorizing the document based on the emotions such as Empathy, Touched, Boredom, Warmness, Amusement and Surprise. In order to predict the emotion contained in content a proposed model i.e. Emotion Topic Model is used. Using this it first generates a latent topic from emotions, followed by generating affective terms from each topic. First it separates emotion and word document and derived probabilities for it. The model which we proposed will utilize the complementary advantages of both emotion-term model and topic model. Emotion-topic model allows associating the terms i.e. words and emotions via topics which is more flexible. For classification we have used Naive Bayesian algorithm and Iteration based Nearest Neighbor Algorithm which will predict emotion accurately. For each emotion, we will be displaying emoticon and songs recommendation will be available for user. So that in future user can upload and enjoy their own choice of song based on their emotion which is detected from text. citation: Miss. Sayalee Sandeep Raut, Vidyalankar Institute of Technology; Prof. Kavita Pankaj Shirsat ,Vidyalankar Institute of Technology. "Implementation of Emotion Generation and Summarization form Affective Text." Global Research and Development Journal For Engineering 18 2016: 58 - 63.