Affective Concept-Based Encoding of Patient Narratives via Sentic Computing and Neural Networks

The automatic generation of features without human intervention is the most critical task for biomedical sentiment analysis. Regarding the high dynamicity of shared patient narrative data, the lack of formal medical language sentiment dictionaries prevents retrieval of the appropriate sentiment, which is unapproachable and can be prone to annotator bias. We propose a novel affective biomedical concept-based encoding via sentic computing and neural networks. The main contributions include four aspects. First, a biomedical embedding, in which a medical entity is defined, normalized, and synthesized from a text, is built using online patient narratives after being combined with label propagation from a widely used comprehensive biomedical vocabulary. Second, considering the dependence on biomedical definitions, drug reaction sample selection based on general matching is suggested. These feature settings are then used to build and recognize affective semantics and sentics based on an extreme learning machine. Finally, a semisupervised LSTM-BiLSTM model for biomedical sentiment analysis is constructed. There was a massive influx of patient self-reports related to the COVID-19 pandemic. A study was conducted in this direction, and we tested the validity, medical language familiarity, and transferability of our approach by analyzing millions of COVID-19 tweets. Comparisons to affective lexicons also indicate that integrating extreme learning machine cognitive capabilities has advantages over biomedical sentiment analysis. By considering sentics vectors on top of the formed embeddings, our semisupervised LSTM-BiLSTM achieved an accuracy of 87.5%. The evaluations of unsupervised learning approximated the results of the previous model when dealing with a serious loss of biomedical data. In this paper, we demonstrate the effectiveness of integrating deep-learning-based cognitive capabilities for both enhancing distributed biomedical definitions and inferring sentiment compositions from many patient self-reports on social networks. The relevant encoding of affective information conveyed regarding medication subjects clearly reveals defined roles and expectations that can have a positive impact on public health.