User Feedback & Analysis with Machine Learning - an experimental study
The objective of this project was to create a method for analyzing the user's feedback of spatial characteristics of a space that they find most attractive and transferring these features to another project.
In order to narrow down the field of research we decided to focus on only one type of space, namely the public squares. We have selected a number of public squares in Spain (located in Madrid, Barcelona, Bilbao and Granada) and around the World in US, UK, and France for this study.
My role in the project was to work on the Natural Language Processing - data collection, data cleaning, creating a ML model & data visualizations. Some images in the project may be low quality or blurry due to output from ML model
Timeline
Mar-May 2020
Team
Sergey Kryuchkov
Pedro Ribeiro
Yara Gadah
Toolkit & techniques
Python
Pytorch
TensorFlow
Numpy
Deliverables
Code
Paper
Github Repo
TL;DR
Incorporating spatial analytics would allow us to gather key feedback & insights from big data. Can such a process be applicable for designing products?
New methodology - We ran the semantic analysis of the user reviews to extract the quality assessment of the urban spaces, then trained the neuron network to recognize the spatial features from the satellite photography and visual graphs based on the space syntax technology to derive conclusions about the relationship between spatial qualities and attractiveness expressed by the verbal assessment
food for thought
Can we adopt such a process to gather user data insights for digital product design projects?
Public square - Place Reial, Barcelona
The attractiveness of a space is a measurable category
We identified correlations between spatial characteristics of the public space. Although the results of the relationships for some matrices were sparse, our experiment demonstrated that the attractiveness of a space is a measurable quantity
Results from the experimental study on a small dataset
METHODOLOGY
How can we assess and encompass a place's spatial features and the feedback of its occupants?
We created a methodology to assess the characteristics of a space by using semantic analysis from social media reviews, spatial features from satellite photography and visual graphs from space syntax application. Following that we added the features weight on a linear scale to get the KPI's as the output
Proposed Spatial design methodology
Process
We ran Polarity/Subjectivity Analysis onto the Dataset for each Public Square to get the overall popularity scores
I trained a LDA model to get coherence values for the number of topics in the data and identified the most popular words in each topic. The PCA analysis showed us which are the most distinctive topics from the dataset and we could extract 5 main categories from the data based on PCA. This was the first part of the analysis. In order to link this data to the features of the image segmentation , we identified the frequency of each popular word in each topic and assigned a value between 0 to 1 based on token values that we got from the PCA analysis. Here you can see the 14 categories of image segmentation that are relatable to the 5 categories of NLP.
Word cloud for the most common words
Sentiment Analysis on the social media reviews
Categorization of texts