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Sentiment analysis or recognizing emotions from short and noisy text from social networks such as twitter has been a challenging task. Most of the existing models use word level embeddings for the final classification of the sentiments. This paper proposes a novel representation of short text derived from a combination of word embeddings and character embeddings using Bidirectional LSTM (BiLSTM)....
Attributes, such as metadata and profile, carry useful information which in principle can help improve accuracy in recommender systems. However, existing approaches have difficulty in fully leveraging attribute information due to practical challenges such as heterogeneity and sparseness. These approaches also fail to combine recurrent neural networks which have recently shown effectiveness in item...
Recurrent neural network has been widely used as auto-regressive model for time series. The most commonly used training method for recurrent neural network is back propagation. However, recurrent neural networks trained with back propagation can get trapped at local minima and saddle points. In these cases, auto-regressive models cannot effectively model time series patterns. In order to address these...
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