Abstract
Medical sentiments derived from health-care related documents, such as health reviews, tweets or forums, have been an indispensable resource for studying insights into patient health conditions and generating additional information for health professionals to provide more supportive treatments. However, approaches implemented in previous studies indicate inadequacy in discovering insights into review details and implicit emotional information due to domain specificities. We propose a sentiment classification framework with medical word embeddings and sequence representation for drug review datasets. Empirical results on different vector transformation methods imply the superiority of sequence incorporated medical sentiment lexicon using machine learning classifiers. Experiments on various word embeddings with convolutional neural network model further justify the effectiveness of medical sentiment word embeddings in sentiment classification for drug reviews.
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Liu, S., Lee, I. (2018). Sentiment Classification with Medical Word Embeddings and Sequence Representation for Drug Reviews. In: Siuly, S., Lee, I., Huang, Z., Zhou, R., Wang, H., Xiang, W. (eds) Health Information Science. HIS 2018. Lecture Notes in Computer Science(), vol 11148. Springer, Cham. https://doi.org/10.1007/978-3-030-01078-2_7
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