Optimization on machine learning based approaches for sentiment analysis on HPV vaccines related tweets.
Optimization on machine learning based approaches for sentiment analysis on HPV vaccines related tweets.
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DOI：
10.1186/s13326-017-0120-6
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发表时间：
2017-03-03
影响因子：
1.9
通讯作者：
Tao C
中科院分区：
文献类型：
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作者：
Du J;Xu J;Song H;Liu X;Tao C
使用基于机器学习的方法分析社交媒体上关于HPV疫苗的公众意见将有助于我们了解疫苗覆盖率低的原因，并提出相应的策略来提高疫苗接种率。提出一个机器学习系统，能够在Twitter上以令人满意的性能提取关于HPV疫苗的综合公众情绪。我们收集并手动注释了6，000条与HPV疫苗相关的推文作为金标准。选择支持向量机模型，提出了一种分层分类方法，并进行了评价。进行了额外的特征集评估和模型参数优化，以最大限度地提高机器学习模型的性能。建立了一个包含10个类别的分层分类方案，以全面了解公众对HPV疫苗的意见。创建了一个6 000条注释推文的黄金语料库，Kappa注释一致率为0.851，并向公众开放。与基线模型相比，优化特征集和模型参数的层次分类模型将微观平均和宏观平均F得分分别从0.6732和0.3967提高到0.7442和0.5883。我们的工作提供了一个系统的方法来提高机器学习模型的性能高度不平衡的HPV疫苗相关的推文语料库。我们的系统可以进一步应用于一个大型的推文语料库，以提取大规模的公众舆论对HPV疫苗。本文的在线版本（doi：10.1186/s13326-017-0120-6）包含补充材料，可供授权用户使用。
Analysing public opinions on HPV vaccines on social media using machine learning based approaches will help us understand the reasons behind the low vaccine coverage and come up with corresponding strategies to improve vaccine uptake. To propose a machine learning system that is able to extract comprehensive public sentiment on HPV vaccines on Twitter with satisfying performance. We collected and manually annotated 6,000 HPV vaccines related tweets as a gold standard. SVM model was chosen and a hierarchical classification method was proposed and evaluated. Additional feature sets evaluation and model parameters optimization was done to maximize the machine learning model performance. A hierarchical classification scheme that contains 10 categories was built to access public opinions toward HPV vaccines comprehensively. A 6,000 annotated tweets gold corpus with Kappa annotation agreement at 0.851 was created and made public available. The hierarchical classification model with optimized feature sets and model parameters has increased the micro-averaging and macro-averaging F score from 0.6732 and 0.3967 to 0.7442 and 0.5883 respectively, compared with baseline model. Our work provides a systematical way to improve the machine learning model performance on the highly unbalanced HPV vaccines related tweets corpus. Our system can be further applied on a large tweets corpus to extract large-scale public opinion towards HPV vaccines. The online version of this article (doi:10.1186/s13326-017-0120-6) contains supplementary material, which is available to authorized users.