Text Processing and Analysis For Arabic-stories

For this project, I developed a robust system to process and analyze Arabic stories using natural language processing (NLP) techniques. The goal was to extract valuable insights from large amounts of text, identify key patterns, and provide data-driven analysis for storytelling trends, sentiment analysis, and linguistic structure.

Key Achievements:

Preprocessing Arabic Text: I implemented various preprocessing techniques such as tokenization, stemming, lemmatization, and stop word removal to clean and prepare the Arabic text for deeper analysis.

Sentiment Analysis: I utilized NLP models to perform sentiment analysis, determining the emotional tone of stories (positive, negative, or neutral).

Keyword Extraction: Using TF-IDF and word frequency methods, I extracted key themes and terms frequently used in Arabic storytelling, providing insights into cultural trends and common narratives.

Linguistic Structure Analysis: I analyzed the structure of sentences, focusing on syntax and morphology, to gain insights into writing styles and complexity across different authors.

Visualization: Created intuitive visualizations to display key findings, such as word clouds, frequency charts, and sentiment distribution graphs, making the data easy to interpret.

This project combined my expertise in text processing and data analysis to deliver a comprehensive analysis of Arabic stories, providing meaningful insights into literary trends and emotional dynamics.