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首页> 外文会议>IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining >Daily life patients Sentiment Analysis model based on well-encoded embedding vocabulary for related-medication text
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Daily life patients Sentiment Analysis model based on well-encoded embedding vocabulary for related-medication text

机译：基于相关药物文本编码良好的嵌入词汇的日常生活患者情感分析模型

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摘要

Millions of health-related messages and fresh communications can reveal important public health issues. New Drugs, Diseases, Adverse Drug Reactions (ADRs) keep appearing on social media in new Unicode versions. In particular, generative Model for both Sentiment analysis (SA) and Naturel Language Understanding (NLU) requires medical human labeled data or making use of resources for weak supervision that operates with the ignorance and the inability to define related-medication targets, and results in inaccurate sentiment prediction performance. The frequent use of informal medical language, nonstandard format and abbreviation forms, as well as typos in social media messages has to be taken into account. We probe the transition-based approach between patients language used in social media messages and formal medical language used in the descriptions of medical concepts in a standard ontology[21] to be formal input of our neural network model. At this end, we propose daily life patients Sentiment Analysis model based on hybrid embedding vocabulary for related-medication text under distributed dependency, and concepts translation methodology by incorporating medical knowledge from social media and real life medical science systems. The proposed neural network layers is shared between medical concept Normalization model and sentiment prediction model in order to understand and leverage related-sentiment information behind conceptualized features in Multiple context. The experiments were performed on various real world scenarios where limited resources in this case.
机译：数以百万计的与健康相关的信息和最新的交流可以揭示重要的公共卫生问题。新药物，疾病，药物不良反应（ADR）一直以新的Unicode版本出现在社交媒体上。尤其是，情感分析（SA）和自然语言理解（NLU）的生成模型需要医学上带有人类标签的数据或利用资源进行薄弱的监督，而这种监督由于无知且无法定义相关的药物治疗目标而导致情绪预测表现不准确。必须考虑非正式医疗语言，非标准格式和缩写形式的频繁使用以及社交媒体消息中的错别字。我们探讨了基于社交媒体消息中的患者语言与标准本体中用于医学概念描述的形式医学语言之间的基于过渡的方法[21]，作为我们神经网络模型的形式输入。为此，我们提出了基于分布式依存关系下相关药物文本的混合嵌入词汇的日常生活患者情感分析模型，并结合了来自社交媒体和现实医学系统的医学知识，提出了概念翻译方法。所提出的神经网络层在医学概念规范化模型和情感预测模型之间共享，以了解和利用多情境中概念化特征背后的相关情感信息。实验是在各种实际情况下（在这种情况下资源有限）进行的。

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