R-Shief - View Single Post - Semantic Web Analysis of Big Data in Arabic, French, and English

Laila · 18-05-11

This project led by USC's Annenberg Innovation Lab is producing a set of Semantic Analyses on the 2011 Arab Spring by creating a data model that will provide lexical analyses for Arabic-only sites, English-only sites, and Arabic-English sites based on language specific set of rules and dictionaries that are applied. This will enable the Annenberg Innovation Lab and R-Shief to perform significant semantic content analysis on Arabic language on the web.

Using R-Shief’s Twitter hashtag archive, Sakr has determined that the majority of tweets are in Arabic. Without being able to pre-process Arabic language content in its native language, sentiment and semantic analyses cannot be properly performed. Rather, users will only be able to provide the usual content analyses on not-so-usual material. Our collaborative sentiment and semantic analyses will be a great contribution to the scholars, artists and journalists using R-Shief's Twitter data. The end result will be a series of visualizations and extensive semantic analysis of the rapidly unfolding current events in the Middle East.

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Laila Administrator 18-05-11 Tweet	Semantic Web Analysis of Big Data in Arabic, French, and English Project Information Maintenance status: Early Development Development Status: Under Active Development Project Versions: 2.0 Project in development since: February 2011 Site URL: https://www.r-shief.org Description USC’s Annenberg Innovation Lab has hired R-Shief Project Lead Laila Shereen Sakr as a research assistant to work with IBM’s LanguageWare tools in order to extend their semantic content analysis into Arabic and French. This project led by USC's Annenberg Innovation Lab is producing a set of Semantic Analyses on the 2011 Arab Spring by creating a data model that will provide lexical analyses for Arabic-only sites, English-only sites, and Arabic-English sites based on language specific set of rules and dictionaries that are applied. This will enable the Annenberg Innovation Lab and R-Shief to perform significant semantic content analysis on Arabic language on the web. Using R-Shief’s Twitter hashtag archive, Sakr has determined that the majority of tweets are in Arabic. Without being able to pre-process Arabic language content in its native language, sentiment and semantic analyses cannot be properly performed. Rather, users will only be able to provide the usual content analyses on not-so-usual material. Our collaborative sentiment and semantic analyses will be a great contribution to the scholars, artists and journalists using R-Shief's Twitter data. The end result will be a series of visualizations and extensive semantic analysis of the rapidly unfolding current events in the Middle East.
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