Abstract
Nowadays, the amount of user-generated contents (UGCs) or texts has surged exponentially. Therefore, recognizing emotions from these texts can bring about lots of advantages. In this paper, we have proposed a novel model named Deep Rolling to predict emotion for target participant in a multi-participant communication context. First, the proposed method converts a text collection into a set of n-dimension vectors for emotion representation and re-organizes texts into a sequence in time order. Then, Deep Rolling can predict the emotion of target participant corresponding to a future time point. Second, apart from simply taking in texts posted by target participant via LSTM, the proposed method has also incorporated texts posted by other participants at every time step by CNN. In this way, Deep Rolling can predict target participant's emotion by processing emotions from both the target and all the other participants in an ensemble way. Finally, data factorization has also been introduced into Deep Rolling to enhance the overall prediction efficiency. According to experimental results, compared with the state-of-art methods, our proposed model has achieved the best prediction precision on different target participants. At the same time, Deep Rolling has also maintained the prediction efficiency at an acceptable level. (C) 2019 Elsevier Inc. All rights reserved.