Opinion mining from medical forums such as health check-ups is sparking growing interest and a stimulating area for natural language processing. This allows for a better understanding of patient health status and drug reactions while generating new knowledge for health care professionals and drug manufacturers, which helps improve the quality of service and produce more effective treatments. In this paper, the researchers present a framework of opinions classification of drug reviews. The objective of this work is to find the best model for analyzing patients’ emotions about drugs. In this sense, the researchers oppose classical text vectorization methods (bag of words, term frequency-inverse document frequency) and word embedding methods (Word2vec, GloVe) for classical opinion mining face to modern machine learning tools with the Convolutional Neural Network (CNN), the Recurrent Neural Networks (Long Short-term Memory and Bidirectional Long Short-Term Memory). Experiments results show that the best model for drug reviews was achieved by CNN based on the Skip-gram model (85% accuracy). Experiments have led to conclude that the performance of a given model will depend on the type of dataset used, on feature representation and better collaboration between classifiers and feature extraction methods.

中文翻译：

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用于药物评论意见挖掘的机器学习和深度学习框架分析

来自健康检查等医学论坛的意见挖掘正在激发人们对自然语言处理的日益增长的兴趣和刺激领域。这有助于更好地了解患者的健康状况和药物反应，同时为医疗保健专业人员和药物制造商提供新知识，这有助于提高服务质量并产生更有效的治疗。在本文中，研究人员提出了药物评论意见分类的框架。这项工作的目的是找到分析患者对药物情绪的最佳模型。在这个意义上，研究人员反对经典的文本向量化方法（词袋，词频-逆文档频率）和词嵌入方法（Word2vec，GloVe) 用于经典的意见挖掘，面向现代机器学习工具，包括卷积神经网络 (CNN)、循环神经网络（长短期记忆和双向长期短期记忆）。实验结果表明，CNN基于Skip-gram模型（准确率85%）实现了药物评论的最佳模型。实验得出的结论是，给定模型的性能将取决于所使用的数据集类型、特征表示以及分类器和特征提取方法之间的更好协作。