CITYSENT

Sentiment analysis of the Netherlands and Flanders

Bachelor thesis (2020)

Authors

A.M. Pardoel Electrical Engineering, Mathematics and Computer Science
D.B. Goslinga Electrical Engineering, Mathematics and Computer Science
R.D. Kalisvaart Electrical Engineering, Mathematics and Computer Science
C.R. Paulsen Electrical Engineering, Mathematics and Computer Science
S.C.M. de Wolf Electrical Engineering, Mathematics and Computer Science

Contributors

M.A. Migut Computer Science & Engineering-Teaching Team - EEMCS (supervisor 1)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2020 A.M. Pardoel, D.B. Goslinga, R.D. Kalisvaart, C.R. Paulsen, S.C.M. de Wolf
Citysent Sentiment analysis Tweets Urban planning Netherlands Flanders
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Published Date

07-07-2020

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

The department of Urbanism at the TU Delft, our clients, research the sentiment in different places, times, ages or genders and compare them to each other. This report describes the purpose, design, implementation and accuracy of a web tool created to get insights into the sentiment people have, de- ducted from social media. The aim of our project was to make research easy and extracting innovative insights from social media.

In our tool, we analysed tweets and their location to collect information about the sentiment different people have towards places. The implementation of our tool consisted of five main components: (1) Twitter-Kafka, processing the tweets from the tweet data stream to our database, (2) face recognition, used for determining whether a tweet comes from a person instead of a company or organisation and for age and gender inference, (3) sentiment analysis, using machine learning to determine whether a tweet is neutral, negative or positive, (4) REST API, for the connection between the front-end and the back-end and (5) the user interface, in the form of an interactive dashboard.

At the beginning of the project, we set up a pipeline that checks the code on multiple things. The testing of the back-end is based on a Python unit test suit. For the build to succeed, all tests must pass and the total branch coverage must be at least 80%. We used Flake8 and ESlint in our build to ensure code quality at all times.

All of the above-mentioned components are up and running. The clients are now able to research the sentiments of people towards places.