The tracking sentiment of the news entities over time provides important information to governments and enterprises during the decision-making process. Recently, it has attracted the attention of the research community as well due to its popularity in many applications including; tracking news about elections, e-commerce, and e-governance. However, most of the work is focused on English whereas limited contributions have been done for Arabic. Moreover, there are no annotated corpora in the Arabic news domain that can be used to perform the sentiment tracking task. In this research, we present an Arabic news corpus and its associated sentiment tracking system to monitor the sentiments towards news entities in the Arab world. Sentiment classification and Named Entity Recognition techniques are used to prepare the corpus for the tracking task. A sample dataset containing 7200 tweets was manually annotated to be used in building multiple classifiers and annotate more than 2.3M tweets using the semi-supervised technique. The results of sentiment classification by using different machine learning classifiers and internal testing set show that semi-automatically annotated dataset outperforms the manually annotated dataset by 23% and 16% on two-way and three-way classification respectively using F1-score. The tracking results illustrate that over time the sentiment tracking performs well at discovering the most popular entities, from social media and, tracking their shifts in different Arab regions. It can be used to detect the possible reasons for sentiment change over time and, to predict the future sentiment of the news entities.

新闻实体随时间的跟踪情绪在决策过程中为政府和企业提供了重要的信息。最近，由于它在许多应用程序中的流行，也引起了研究界的关注。跟踪有关选举，电子商务和电子政务的新闻。但是，大多数工作集中在英语上，而对阿拉伯文的贡献有限。此外，阿拉伯新闻域中没有可用于执行情感跟踪任务的带注释的语料库。在这项研究中，我们提出了一个阿拉伯新闻语料库及其相关的情绪跟踪系统，以监视对阿拉伯世界新闻实体的情绪。情感分类和命名实体识别技术用于为跟踪任务准备语料库。手动注释了包含7200条推文的样本数据集，以用于构建多个分类器，并使用半监督技术对超过2.3M条推文进行注释。使用不同的机器学习分类器和内部测试集进行情感分类的结果表明，在使用F1评分的双向和三向分类中，半自动注释的数据集分别比手动注释的数据集分别高23％和16％。跟踪结果表明，随着时间的流逝，情绪跟踪在从社交媒体发现最受欢迎的实体以及跟踪其在阿拉伯地区的变化方面表现良好。它可用于检测情绪随时间变化的可能原因，并预测新闻实体的未来情绪。