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The authors found that the frequency, magnitude, and pattern of seasonality of mood in African American students were similar to those previously reported in the general population at similar latitude, but that awareness of the existence of seasonal affective disorder, a condition with safe and effective treatment options, was lower.

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This paper introduces a novel deep learning framework including a lexicon-based approach for sentencelevel prediction of sentiment label distribution. We propose to first apply semantic rules and then use a Deep Convolutional Neural Network (DeepCNN) for character-level embeddings in order to increase information for word-level embedding. After that, a Bidirectional Long Short-Term Memory network (Bi-LSTM) produces a sentence-wide feature representation from the word-level embedding. We evaluate our approach on three twitter sentiment classification datasets. Experimental results show that our model can improve the classification accuracy of sentence-level sentiment analysis in Twitter social networking.

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