Corpus Explorer
Inspect Arabic datasets and prepare them for analysis.
Arabic language, reimagined with AI
One AI workspace for everything you do with Arabic language.
Upload Arabic data, explore language patterns, build intelligent workflows, and turn text into meaningful insights.
Uploaded dataset
arabic_reviews.xlsxDataset understood
Recommended next step
Build a sentiment analysis workflowYour file is ready for a guided classification experiment.
A workspace built around your goal
Start with the outcome you need. LinguaLab connects the right language tools behind the scenes.
Build
Generate practical code for Arabic NLP, data analysis, and research tasks with explanations and setup guidance.
Open AI Coding Assistant 02Analyze
Access connected tools for frequency, concordance, n-grams, classification, and other Arabic text analysis tasks.
Open Research Hub 03Learn
Move from concepts to practical experiments through a guided learning dashboard designed for students.
Open Learning Dashboard 04Discover
Analyze Arabic text, interpret key results, and identify a clear next step for your research project.
Open AI Analysis PlannerOne connected journey
LinguaLab replaces scattered spreadsheets, notebooks, scripts, and chat windows with a guided path from data to decision.
Everything you need, connected
The tools remain available, but they now serve the user's goal instead of defining the product.
Inspect Arabic datasets and prepare them for analysis.
Find repeated words and emerging language patterns.
Study a word through the contexts in which it appears.
Discover recurring phrases and multi-word expressions.
Explore grammatical categories in Arabic text.
Build and understand text-classification experiments.
Generate a practical starting point for analysis code.
Ask better questions and decide what to do next.
Meet your AI research partner
LinguaLab examines the data, highlights what matters, and helps the user choose a sound next step.
Meet the assistantI found a text column and a sentiment label. Your classes are slightly imbalanced, so I recommend reviewing the distribution before training the model.
Start with the language. End with insight.