• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE International Conference on Data Science and Advanced Analytics >Monitoring the Twitter sentiment during the Bulgarian elections
【24h】

Monitoring the Twitter sentiment during the Bulgarian elections

机译：在保加利亚大选期间监控Twitter情绪

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

We present a generic approach to real-time monitoring of the Twitter sentiment and show its application to the Bulgarian parliamentary elections in May 2013. Our approach is based on building high quality sentiment classification models from manually annotated tweets. In particular, we have developed a user-friendly annotation platform, a feature selection procedure based on maximizing prediction accuracy, and a binary SVM classifier extended with a neutral zone. We have also considerably improved the language detection in tweets. The evaluation results show that before and after the Bulgarian elections, negative sentiment about political parties prevailed. Both, the volume and the difference between the negative and positive tweets for individual parties closely match the election results. The later result is somehow surprising, but consistent with the prevailing negative sentiment during the elections.
机译：我们提供了一种实时监控Twitter情绪的通用方法，并展示了其在2013年5月的保加利亚议会选举中的应用。我们的方法基于通过手动注释的推文构建高质量的情绪分类模型。特别是，我们开发了一个用户友好的注释平台，一个基于最大化预测准确性的特征选择程序以及一个扩展了中性区域的二进制SVM分类器。我们还大大改善了推文中的语言检测。评价结果表明，在保加利亚大选前后，对政党的消极情绪盛行。各个政党的负面和正面推文的数量和差异都与选举结果紧密匹配。后来的结果某种程度上令人惊讶，但与选举期间普遍存在的负面情绪一致。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号