Abstract
Discussion features in online communities can be effectively used to diagnose depression and allow other users or experts to provide self-help resources to those in need. Automatic emotion identification models can quickly and effectively highlight indicators of emotional stress in the text of such discussions. Such communities also provide patients with important knowledge to help better understand their condition. This study proposes a deep learning framework combining word embeddings, bi-directional long short-term memory (Bi-LSTM), and convolutional neural networks (CNN) to identify emotion labels from psychiatric social texts. The Bi-LSTM is a powerful mechanism for extracting features from sequential data in which a sentence consists of multiple words in a particular sequence. CNN is another powerful feature extractor which can convolute many blocks to capture important features. Our proposed deep learning framework also applies word representation techniques to represent semantic relationships between words. The paper thus combines two powerful feature extraction methods with word embedding to automatically identify indicators of emotional stress. Experimental results show that our proposed framework outperformed other models using traditional feature extraction such as bag-of-words (BOW), latent semantic analysis (LSA), independent component analysis (ICA), and LSA+ICA.
Highlights
Rather than seek professional help, people suffering from mental illness or emotional strain often turn to online communities in search of advice or a sense of human intimacy and understanding
BI-long short-term memory (LSTM)-convolutional neural networks (CNN) MODEL FOR EMOTION LABEL IDENTIFICATION We propose a deep learning framework for multiple emotion label identification using two powerful feature extractors with a word presentation approach
We propose a deep neural network model comprising six layers of neural networks including a word embedding layer, a bi-directional long short-term memory (Bi-LSTM) layer, a CNN layer, a maxpooling layer, a hidden layer, and an inference layer
Summary
Rather than seek professional help, people suffering from mental illness or emotional strain often turn to online communities in search of advice or a sense of human intimacy and understanding. Convolutional neural networks (CNN), and long short-term memory (LSTM) NNs are used to build a powerful classifier to identify emotion labels. These models have been successfully applied in a wide range of categorization tasks and are used here to develop a powerful classifying mechanism for multiple emotion labels within psychiatric social texts based on the classification performance of two factors: word embeddings and neural network architectures. BI-LSTM-CNN MODEL FOR EMOTION LABEL IDENTIFICATION We propose a deep learning framework for multiple emotion label identification using two powerful feature extractors with a word presentation approach. We use the GloVe tool to create the word embeddings for the emotion label detection
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