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首页> 外文期刊>International journal of operations research and information systems >Negation Handling in Machine Learning-Based Sentiment Classification for Colloquial Arabic
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Negation Handling in Machine Learning-Based Sentiment Classification for Colloquial Arabic

机译：基于机器学习的情绪的否定处理对口语阿拉伯语的语言分类

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摘要

One crucial aspect of sentiment analysis is negation handling, where the occurrence of negation can flip the sentiment of a review and negatively affects the machine learning-based sentiment classification. The role of negation in Arabic sentiment analysis has been explored only to a limited extent, especially for colloquial Arabic. In this paper, the authors address the negation problem in colloquial Arabic sentiment classification using the machine learning approach. To this end, they propose a simple rule-based algorithm for handling the problem that affects the performance of a machine learning classifier. The rules were crafted based on observing many cases of negation, simple linguistic knowledge, and sentiment lexicon. They also examine the impact of the proposed algorithm on the performance of different machine learning algorithms. Furthermore, they compare the performance of the classifiers when their algorithm is used against three baselines. The experimental results show that there is a positive impact on the classifiers when the proposed algorithm is used compared to the baselines.
机译：情感分析的一个关键方面是否定处理，其中否定的发生可以让审查的情绪翻转和对基于机器学习的情感分类进行负面影响。否定在阿拉伯语情绪分析中的作用仅在有限的程度上探讨，特别是对于口语阿拉伯语。在本文中，作者用机器学习方法解决了口语阿拉伯语情绪分类中的否定问题。为此，他们提出了一种简单的规则的算法来处理影响机器学习分类器性能的问题。根据观察许多否定，简单的语言知识和情绪词典，根据观察许多案例制作规则。他们还研究了所提出的算法对不同机器学习算法性能的影响。此外，它们比较分类器的性能，当它们的算法用于三个基线时。实验结果表明，当与基线相比，使用所提出的算法时，对分类器存在正影响。

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