The talk will focus on the problem of embedding human language complexities in the framework of Object Oriented Data Analysis, showing a novel application of these statistical techniques to textual data. Starting from the investigation of the vast literature dealing with natural language understanding, we will define a taxonomy of mathematical models aimed at embedding complex properties of text. Along the talk, we will also discuss the potentials of the statistical analysis of the content of social media messages to support the landscape design process, showing the results of the analysis of Tweets collected from the Queen Elizabeth Olympic Park in London. This contributes to the conversation on the employment of Big Data analytical techniques to study the users perception of landscape.

An Object Oriented Data Analysis of Tweets: the Case of Queen Elizabeth Olympic Park Object Oriented Data Analysis di Tweet: il caso del Queen Elizabeth Olympic Park

Paola Riva;Paola Sturla;Anna Calissano;Simone Vantini
2019

Abstract

The talk will focus on the problem of embedding human language complexities in the framework of Object Oriented Data Analysis, showing a novel application of these statistical techniques to textual data. Starting from the investigation of the vast literature dealing with natural language understanding, we will define a taxonomy of mathematical models aimed at embedding complex properties of text. Along the talk, we will also discuss the potentials of the statistical analysis of the content of social media messages to support the landscape design process, showing the results of the analysis of Tweets collected from the Queen Elizabeth Olympic Park in London. This contributes to the conversation on the employment of Big Data analytical techniques to study the users perception of landscape.
Book of Short Papers SIS2019
9788891915108
Object Oriented Data Analysis, Statistical Text Analysis, Text Embedding, Digital Landscape Architecture, Landscape Perception, Queen Elizabeth Olympic Park
File in questo prodotto:
File Dimensione Formato  
ISTITUZIONI - HE - PDF - SIS 2019 - V2.pdf

Accesso riservato

Descrizione: Book of papers
: Publisher’s version
Dimensione 128.57 MB
Formato Adobe PDF
128.57 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1210780
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact