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Sentiment Analysis of Iraqi Arabic Dialect on Facebook Based on Distributed Representations of Documents

机译：基于文档分布式表示的伊拉克阿拉伯方言情感分析

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摘要

Nowadays, social media is used by many people to express their opinions about a variety of topics. Opinion Mining or Sentiment Analysis techniques extract opinions from user generated contents. Over the years, a multitude of Sentiment Analysis studies has been done about the English language with deficiencies of research in all other languages. Unfortunately, Arabic is one of the languages that seems to lack substantial research, despite the rapid growth of its use on social media outlets. Furthermore, specific Arabic dialects should be studied, not just Modern Standard Arabic. In this paper, we experiment sentiments analysis of Iraqi Arabic dialect using word embedding. First, we made a large corpus from previous works to learn word representations. Second, we generated word embedding model by training corpus using Doc2Vec representations based on Paragraph and Distributed Memory Model of Paragraph Vectors (DM-PV) architecture. Lastly, the represented feature used for training four binary classifiers (Logistic Regression, Decision Tree, Support Vector Machine and Naive Bayes) to detect sentiment. We also experimented different values of parameters (window size, dimension and negative samples). In the light of the experiments, it can be concluded that our approach achieves a better performance for Logistic Regression and Support Vector Machine than the other classifiers.
机译：如今，社交媒体被许多人用来表达他们对各种主题的看法。意见挖掘或情感分析技术从用户生成的内容中提取意见。多年来，已经对英语进行了许多情感分析研究，但所有其他语言的研究都存在不足。不幸的是，尽管阿拉伯语在社交媒体上的使用迅速增长，但它却是缺乏实质性研究的语言之一。此外，应该研究特定的阿拉伯方言，而不仅仅是现代标准阿拉伯语。在本文中，我们通过词嵌入实验对伊拉克阿拉伯方言进行了情感分析。首先，我们从以前的作品中获得了大量的语料库来学习单词表示。其次，基于段落和段落向量的分布式存储模型（DM-PV）体系结构，我们使用Doc2Vec表示通过训练语料库来生成单词嵌入模型。最后，所表示的特征用于训练四个二进制分类器（逻辑回归，决策树，支持向量机和朴素贝叶斯）以检测情绪。我们还尝试了不同的参数值（窗口大小，尺寸和负样本）。根据实验，可以得出结论，我们的方法在Logistic回归和支持向量机方面比其他分类器具有更好的性能。

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