ONE : Toward ONE model, ONE algorithm, ONE corpus dedicated to sentiment analysis of A rabic/ A rabizi and its dialects (2021)

Abstract: Arabic is the official language of 22 countries, spoken by more than 400 million speakers. Each one of this country use at least on dialect for daily life conversation. Then, Arabic has at least 22 dialects. Each dialect can be written in Arabic or Arabizi Scripts. The most recent researches focus on constructing a language model and a training corpus for each dialect, in each script. Following this technique means constructing 46 different resources (by including the Modern Standard Arabic, MSA) for handling only one language. In this paper, we extract ONE corpus, and we propose ONE algorithm to automatically construct ONE training corpus using ONE classification model architecture for sentiment analysis MSA and different dialects. After manually reviewing the training corpus, the obtained results outperform all the research literature results for the targeted test corpora.

## introduction all the survey works in the literature @xcite @xcite classify arabic in three main varieties: 1) classical arabic (ca), 2) modern standard arabic (msa) and 3) dialectal arabic @xcite . arabic dialects are another form of arabic used in daily life communication. each dialect shares many features with msa, but they globally differ in some aspects. arabic and its dialects can be written either in arabic script or in arabizi one. arabizi is a form of writing arabic text that relies on latin letters, numerals and punctuation rather than arabic letters @xcite . for ex-ample, the arabic sentence: this paper proposes a general algorithm constructing a language model from a large corpus and a training corpus automatically to bridge the gap. it also proposes the transliteration of the arabizi messages into arabic. this approach was applied to algerian dialect (a maghrebi dialect), having a lack of resources. however, the constructed model was used for classifying the sentiment of messages written in msa, tunisian dialect or even egyptian dialect. the results were very encouraging. however, the manual review of a small part of the training corpus constructed automatically leads to outperform all the research literature results for the testing corpora cited above. ## the research works inspiring the proposed work the aim of the proposed model is to analyse the sentiment of arabic message (written with both arabic/arabizi scripts). in this context, we need to focus on three categories of works: 1) works on arabizi transliteration. 2) works on lexicon-based approach. ## conclusion the major contribution in this paper is the new perspectives that it opens: moreover, only simple word embedding models were used (word2vec and fasttext). it was for showing the efficacy of the approach even with the fastest models. however, in the future, we are planning to improve this approach with the most recent models such as bert @xcite or @xcite .