论文标题
阿拉伯语中的多级情绪分析
Multilevel sentiment analysis in arabic
论文作者
论文摘要
在这项研究中，我们旨在提高阿拉伯情感分析的绩效结果。这可以通过研究最成功的机器学习方法和最有用的功能向量来实现这一目标，以将术语和文档级别中的情感分类为两个（正或负面）类别。此外，研究了一个以上的术语的一个极性程度的规范。为了处理否定和强化，制定了一些规则。根据获得的结果，人工神经网络分类器被提名为阿拉伯语的学期和文档级别情感分析（SA）中的最佳分类器。此外，正级和负测试类别中术语级别SA中达到的平均F得分为0.92。在文档级别的SA中，阳性测试类别的平均F得分为0.94，而负类的平均F得分为0.93。
In this study, we aimed to improve the performance results of Arabic sentiment analysis. This can be achieved by investigating the most successful machine learning method and the most useful feature vector to classify sentiments in both term and document levels into two (positive or negative) categories. Moreover, specification of one polarity degree for the term that has more than one is investigated. Also to handle the negations and intensifications, some rules are developed. According to the obtained results, Artificial Neural Network classifier is nominated as the best classifier in both term and document level sentiment analysis (SA) for Arabic Language. Furthermore, the average F-score achieved in the term level SA for both positive and negative testing classes is 0.92. In the document level SA, the average F-score for positive testing classes is 0.94, while for negative classes is 0.93.
