Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts.
Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts.
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DOI：
10.1016/j.jbi.2016.06.007
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发表时间：
2016-08
影响因子：
4.5
通讯作者：
Gonzalez GH
中科院分区：
文献类型：
--
作者：
Korkontzelos I;Nikfarjam A;Shardlow M;Sarker A;Ananiadou S;Gonzalez GH
情感分析功能对于发现文本中的药物不良反应很有用。情绪分析功能有助于区分药物不良反应和适应症。关于药物不良反应的帖子与负面情绪有关。社交媒体和健康相关论坛沿着的大量文本以及公众意见的丰富表达，最近吸引了公共卫生界的兴趣，将这些来源用于药物警戒。基于直觉，患者发布关于药物不良反应（ADR）表达负面情绪，我们调查情感分析功能在定位ADR提及的效果。我们使用情感分析特征丰富了最先进的ADR识别方法的特征空间。使用来自DailyStrength论坛的帖子和注释ADR和指示提及的推文的语料库，我们评估情感分析功能在多大程度上有助于定位ADR提及并将其与指示提及区分开来。评估结果表明，情感分析功能稍微提高了ADR识别的推文和健康相关的论坛帖子。添加情感分析功能实现了统计上显着的F-测量增加从72.14%到73.22%，在Twitter部分的现有语料库使用其原始的训练/测试分裂。使用分层的10 × 10倍交叉验证，在语料库的DailyStrength部分，从79.57%增加到80.14%，在语料库的Twitter部分，从66.91%增加到69.16%。此外，情感分析功能被证明可以减少被识别为指示的ADR的数量。这项研究表明，添加情感分析功能可以稍微提高性能，即使是最先进的ADR识别方法。由于社交媒体和健康论坛的迅速普及，这一改进可用于药物警戒实践。
Sentiment analysis features are useful in spotting adverse drug reactions in text. Sentiment analysis features help to distinguish adverse drug reactions and indications. Posts about adverse drug reactions are associated with negative feelings. The abundance of text available in social media and health related forums along with the rich expression of public opinion have recently attracted the interest of the public health community to use these sources for pharmacovigilance. Based on the intuition that patients post about Adverse Drug Reactions (ADRs) expressing negative sentiments, we investigate the effect of sentiment analysis features in locating ADR mentions. We enrich the feature space of a state-of-the-art ADR identification method with sentiment analysis features. Using a corpus of posts from the DailyStrength forum and tweets annotated for ADR and indication mentions, we evaluate the extent to which sentiment analysis features help in locating ADR mentions and distinguishing them from indication mentions. Evaluation results show that sentiment analysis features marginally improve ADR identification in tweets and health related forum posts. Adding sentiment analysis features achieved a statistically significant F-measure increase from 72.14% to 73.22% in the Twitter part of an existing corpus using its original train/test split. Using stratified 10 × 10-fold cross-validation, statistically significant F-measure increases were shown in the DailyStrength part of the corpus, from 79.57% to 80.14%, and in the Twitter part of the corpus, from 66.91% to 69.16%. Moreover, sentiment analysis features are shown to reduce the number of ADRs being recognized as indications. This study shows that adding sentiment analysis features can marginally improve the performance of even a state-of-the-art ADR identification method. This improvement can be of use to pharmacovigilance practice, due to the rapidly increasing popularity of social media and health forums.