The Easy Way to
Your Data

Guiding you to build data science projects remarkably fast, collaboratively and without writing code.
More powerful than dashboards and more intuitive than notebooks.

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Inside Graphext.

The Graph
Compare
Trends
Details
Insights
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Connect Data.

Pull data directly from databases like PostgreSQL, MySQL, Google BigQuery and more. Integrate with Google Drive and Sheets.
Drag and drop files into your Graphext workspace to start working with datasets. We support a range of file types including CSV, JSON and SAV.
Use Tractor to create new datasets from information posted on popular platforms including Twitter and Google News.
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Build Projects.

Create and deploy powerful clustering and prediction models on your data using the guidance of our setup wizard.
Filter and sample your data directly from your Graphext workspace. Switch and control the variable types in your data.
Enrich your analysis with information from popular APIs or external sources with census, climate or socio-demographic data.
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Explore & Analyze.

Discover communities and their features using the visual representation of the Graph to explore your datasets.
Find temporal patterns in your data at a glance. Switch between chart types and detect anomalies and trends automatically.
Uncover the relevance of variables in explaining similarities and differences between segments of your data.
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Report Insights.

Capture your findings as you move through your analysis. Create reports to help tell the stories in your data.
Save and download charts, tables and insights as exportable image files.
Publish and present your Graphext project. Embed projects in any website to start sharing aspects of your analysis online.
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Resources

Sentiment Analysis & Billboard Top 100: The Changing Mood of Popular Music

We used sentiment analysis to model 5100 Billboard chart-toppers between 1964 and 2015. Our analysis predicted whether song lyrics were positive, negative or neutral as well as detecting the topic and intent behind the most popular tunes in music history.

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The 5 Most Extreme US Office Characters

Testing out our brand spanking new integration with Hugging Face models for NLP, we analyzed speech from characters in all 9 series of the US Office. Added into our Graphext project, the language models focused on classifying the dialogue of Michael, Dwight, Pam, Jim, Daryll and all the other characters according to the detection of sentiment, emotion, offensive language, irony and hate speech.

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How to Study Brand Conversations with Advanced Text Analysis?

How can we use text analysis of data from Twitter to improve our understanding of markets? This is the question prompting Paul, a strategist in our business team, to scrape tweets about Lloyds bank and conduct a Twitter topic analysis using advanced NLP and network creation. First, he collected tweets using Tractor, Graphext's scraping tool for social media analysis. Then, he analyzed the topics of tweets using network analysis. Here's how he did it ...

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A Beginners Guide to Market Segmentation: Types, Techniques & Examples to Better Understand Your Customer Base (with Data)

Market segmentation means splitting your customer base into distinct communities based on the similarity of their features. Depending on the data you use to segment customers, clustering a market dataset results in the grouping of customers based on geographic, demographic, behavioural and psychographic factors as well as their buying preferences.

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A digest of our blog data analysis, product updates and company news
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