RealSoft Announces a New Research
RealSoft is pleased to announce a new research paper titled “Using Machine Learning Approaches for Economic Classification Based on Arabic Textual Descriptions.” Authored by Jaffar Mansour and Fatima Al Taharwah, the paper addresses the challenges encountered in statistical survey analysis when classifying Arabic text data according to classification schemes. The research offers valuable insights and proposes machine learning techniques tailored for economic classification using Arabic textual descriptions. RealSoft is proud to contribute to the field with this study, which aims to enhance data analysis and improve decision-making processes in economics.
In the world of statistical analysis, open-ended questions have long been a source of valuable but time-consuming data. Extracting insights from such data often relies on manual interpretation and analysis, leading to increased effort and time as the volume of data grows. This research aims to revolutionize this process by leveraging artificial intelligence and data science techniques for automated classification of textual data.
The focal point of this study is the Arabic language, given its prevalence in statistical research across the region. Statistical centers frequently collect textual data on the economic activity of establishments and individuals through population censuses, economic surveys, and various social and economic studies. However, classifying this data according to the International Standard Industrial Classification (ISIC) Rev. 4 poses significant challenges and requires the expertise of specialists.
RealSoft’s research paper explores the use of natural language processing techniques and text classification methods to automatically classify economic activity textual data in Arabic. By examining the accuracy of data cleaning and normalization, as well as classification techniques, the paper demonstrates the potential of machine learning in streamlining and enhancing the accuracy of economic classification.
RealSoft is proud to have presented this research at the 7th Union of Arab Statisticians conference, where it garnered immense interest and appreciation from industry experts and statisticians alike.
To access the full research paper, please Click Here.