• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE/ACS International Conference on Computer Systems and Applications >SVM based approach for opinion classification in Arabic written tweets
【24h】

SVM based approach for opinion classification in Arabic written tweets

机译：基于SVM的阿拉伯文书面推文中的意见分类方法

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

摘要

We propose a machine learning approach for automatically classifying opinions of Twitter texts written in Modern Standard Arabic (MSA). Tweets are classified as either positive, negative, neutral or non-opinion. Various features for opinion classification have been used which are mainly linguistic and numeric. Our in-house collected and developed training data consists of tweets preserving their specifications such as @usermentions, #hashtags which are used as tweet-particular features. Four machine learning algorithms were applied on our dataset: Support Vector Machine (SVM), Naive Bayes (NB), J48 decision tree and Random forest. The experiments results show that SVM gives the highest F measure (72%), while the j48 classifier gives the highest precision (70,97%). Our experimental results demonstrate that tweet's specific features can significantly improve classification performance in comparison to other features combination.
机译：我们提出了一种机器学习方法，用于自动分类以现代标准阿拉伯语（MSA）编写的Twitter文本的观点。推文分为正面，负面，中性或非观点。意见分类的各种功能已被使用，主要是语言和数字功能。我们内部收集和开发的培训数据包括保持其规范的推文，例如@ usermentions，＃hashtags（这些推文是推特特有的功能）。四种机器学习算法应用于我们的数据集：支持向量机（SVM），朴素贝叶斯（NB），J48决策树和随机森林。实验结果表明，SVM提供了最高的F度量（72％），而j48分类器提供了最高的精度（70.97％）。我们的实验结果表明，与其他功能组合相比，tweet的特定功能可以显着改善分类性能。

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