Learning implicit sentiments in Alzheimer's disease recognition with contextual attention features - Liu, Ning; Yuan, Zhenming; Chen, Yan; Liu, Chuan; Wang, Lingxing (Frontiers in Aging Neuroscience, 2023)
Background: Alzheimer’s disease(AD) is difficult to be diagnosed based on language because of the implicit emotion of transcripts, AD recognition is a supervised fuzzy implicit emotion classification with document level. Recent neural network-based approaches has paid no attention to the implicit sentiment entailed in AD transcripts. Method: In order to detect deep semantic information, a two-level attention mechanism is proposed, enabling it can attend different important contents as constructing the document representation. To be specific, the document vector is built by aggregating important words to sentence vectors and important sentences into document vectors progressively. Results: Experiment results show that our method achieves a best 88.28% accuracy on annotated Pitt corpora, which validates its effective of learning implicit sentiment representation for our model. Conclusion: The proposed model can select informative words and sentences qualitatively by attention layers, and this method gives a good inspiration for AD diagnosis based on implicit sentiment transcripts.