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The main stay of this project is to find the connections between emotions and affective terms by categorizing the web-content, based on the emotion present in it and also predicting the emotions from text automatically. Emotions can provide a new aspect for categorizing different documents, and therefore it will be useful for online users to select related documents based on their emotional preferences. In order to predict the emotion contained in a text, a joint emotion topic model by augmenting Latent Dirichlet Allocation (LDA) with an additional layer for emotion modeling is used. Using this, it first generates a set of latent topics from emotions, followed by generating emotional terms from each topic., which finally provides a specific emotion for a particular content or text from a document-specific emotional distribution. The emotion-topic model utilizes the complementary advantages of both emotion-term model and emotion-topic model. Emotion-topic model allows associating the terms and emotions via topics which is more flexible and has better modeling potential. This model notably improves the performance of social emotion prediction.
