文章基本信息

标题：Enhance Medical Sentiment Vectors through Document Embedding using Recurrent Neural Network
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作者：Rami N. M. Yousef ; Sabrina Tiun ; Nazlia Omar 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2020
卷号：11
期号：4
DOI：10.14569/IJACSA.2020.0110452
出版社：Science and Information Society (SAI)
摘要：Adverse Drug Reaction (ADR) extraction is the process of identifying drug implications mentioned in social posts. Handling medical text for the identification of ADR is vital to research in terms of configuring the side effect and other medical-related entities within any medical text. However, investigating the role of such effect in the context of positive and negative is the responsibility of sentiment classification task where every medical review document would be categorized into its polarity, this is known as Medical Sentiment Analysis (MSA). Several studies have presented various techniques for MSA. Most of the recent studies have concentrated on architectures such as the Convolutional Neural Network (CNN) to get the document embedding. Yet, such architecture focuses only on the input without considering the previous or latter input. This might lead to weaker embedding for the document where some terms would not be considered. Hence, this paper proposes a new document embedding approach based on the Recurrent Neural Network (RNN) to improve the sentiment classification. Using a benchmark dataset of medical sentiments, the proposed method showed greater performance of sentiment classification accuracy. Such finding proves the effectiveness of RNN in producing document embedding.
关键词：Adverse drug reaction; medical sentiment analysis; recurrent neural network; support vector machine; logistic regression