Muppalla Roopteja, Miller Michele, Banerjee Tanvi, Romine William
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:1194-1197. doi: 10.1109/EMBC.2017.8037044.
Zika virus has caught the worlds attention, and has led people to share their opinions and concerns on social media like Twitter. Using text-based features, extracted with the help of Parts of Speech (POS) taggers and N-gram, a classifier was built to detect Zika related tweets from Twitter. With a simple logistic classifier, the system was successful in detecting Zika related tweets from Twitter with a 92% accuracy. Moreover, key features were identified that provide deeper insights on the content of tweets relevant to Zika. This system can be leveraged by domain experts to perform sentiment analysis, and understand the temporal and spatial spread of Zika.
寨卡病毒引起了全球关注，并促使人们在推特等社交媒体上分享他们的看法和担忧。利用词性标注器（POS）和N元语法提取的基于文本的特征，构建了一个分类器，用于从推特中检测与寨卡相关的推文。通过一个简单的逻辑分类器，该系统成功地从推特中检测出与寨卡相关的推文，准确率达92%。此外，还识别出了关键特征，这些特征能更深入地洞察与寨卡相关推文的内容。该领域的专家可以利用这个系统进行情感分析，并了解寨卡病毒的时空传播情况。
