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This dataset has been created between 2017 and 2021 to provide a textual resource that can be used to study the behaviors of Tunisian people in writing Tunisian Arabic (ISO 693-3: aeb) in Latin Script. This corpus is constituted from messages written using Tunisian Arabic Chat Alphabet or Arabizi and is developed to solve the matter of the lack of NLP databases about the use of the Latin Script for transcribing Tunisian Arabic. The messages are automatically pulled using web scraping of Facebook public pages and are kept as they are without any annotation, spelling adjustments or morphological and syntactic labeling. Then, messages that are written in Latin Script but not in Tunisian Arabic are manually eliminated. Finally, every collection of messages that are retrieved from the same Facebook page in the same period is included in the same text file where every message is featured as one line.
This corpus has been developed by the Data Engineering and Semantics Research Unit (DES-Unit), University of Sfax, Tunisia. It has been developed to increase the coverage of Latin Script in the NLP resources for Tunisian. It is included as a part of the Tunisian Arabic Corpus (http://www.tunisiya.org/).
Tunisian Arabic, Latin Script, Arabizi, Corpus, Arabic Chat Alphabet
Tunisian Arabic, Latin Script, Arabizi, Corpus, Arabic Chat Alphabet
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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