See us in action making the most out of data.

El pasado 15 de julio estuvimos en directo con Juan Palacios, CEO y fundador de D'Anchiano. Juan nos contó en detalle el uso que le da a Graphext para procesos de selección, así como una mayor visibilidad de qué utilidad tiene Graphext y el análisis de datos en RRHH y detección de talento.

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.

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.

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 ...