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首页> 外文期刊>Inteligencia Artificial : Ibero-American Journal of Artificial Intelligence >Arabic dialect sentiment analysis with ZERO effort. Case study: Algerian dialect
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Arabic dialect sentiment analysis with ZERO effort. Case study: Algerian dialect

机译:阿拉伯语方言情绪分析零努力。案例研究:阿尔及利亚方言

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This paper presents an analytic study showing that it is entirely possible to analyze the sentiment of an Arabic dialect without constructing any resources. The idea of this work is to use the resources dedicated to a given dialect extit{X} for analyzing the sentiment of another dialect extit{Y}. The unique condition is to have extit{X} and extit{Y} in the same category of dialects. We apply this idea on Algerian dialect, which is a Maghrebi Arabic dialect that suffers from limited available tools and other handling resources required for automatic sentiment analysis. To do this analysis, we rely on Maghrebi dialect resources and two manually annotated sentiment corpus for respectively Tunisian and Moroccan dialect. We also use a large corpus for Maghrebi dialect. We use a state-of-the-art system and propose a new deep learning architecture for automatically classify the sentiment of Arabic dialect (Algerian dialect). Experimental results show that F1-score is up to 83% and it is achieved by Multilayer Perceptron (MLP) with Tunisian corpus and with Long short-term memory (LSTM) with the combination of Tunisian and Moroccan. An improvement of 15% compared to its closest competitor was observed through this study. Ongoing work is aimed at manually constructing an annotated sentiment corpus for Algerian dialect and comparing the results.
机译:本文提出了一个分析研究,表明它完全可以分析阿拉伯语方言的情绪而不构建任何资源。这项工作的想法是使用专用于给定的语句 TextIT {X}的资源来分析另一个方言 Texit {Y}的情绪。唯一条件是在相同类别的方言中具有 textit {x}和 textit {y}。我们对阿尔及利亚方言进行了这个想法,这是一个Maghrebi阿拉伯语方言,患有有限的可用工具和其他自动情绪分析所需的处理资源。为此分析,我们依靠Maghrebi方言资源和分别为突尼斯和摩洛哥方言的两种手动被引导的情绪语料库。我们还使用Magrebi方言的大型语料库。我们使用最先进的系统,并提出了一种新的深度学习架构,可自动对阿拉伯语方言(阿尔及利亚方言)的情绪进行分类。实验结果表明,F1分数高达83%,通过突尼斯语料库的多层感知者(MLP)和长期内存(LSTM)实现,具有突尼斯和摩洛哥的长期内存(LSTM)。通过本研究观察到与最接近竞争对手相比的15%的提高。正在进行的工作旨在手动构建Algerian方言的被带电情绪语料库,并比较结果。

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