Upotreba strojnog učenja u procjeni osobnosti

undergraduate thesis

• Title (english): The use of machine learning in personality assessment
• Author: Ivan Kušeta
• Mentor: Marina Bagić Babac (mentor)
• Committee member: Marina Bagić Babac (predsjednik povjerenstva)
• Committee member: Marko Horvat (član povjerenstva)
• Committee member: Mihael Kovač (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2022-09-09, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

Cilj ovog završnog rada je istražiti učinkovitost algoritama strojnog učenja u procjeni tipova osobnosti. Prediktivna analiza temeljit će se na javno dostupnom označenom skupu podataka koji sadrži tekstove korisnika sa društvenih mreža zajedno s njihovim tipovima osobnosti koji su izraženi pomoću Myers Briggs indikatora. Korištenjem obrade prirodnog jezika potrebo je predobraditi skup podataka te izdvojiti relevantne značajke sadržaja za daljnju obradu. Zatim treba primijeniti odgovarajuće klasifikacijske algoritme u svrhu procjene osobnosti. Potrebno je prikazati i obrazložiti rezultate različitih algoritama strojnog učenja zajedno s njihovim prednostima i nedostacima.

Abstract (english)

The goal of this thesis is to explore the effectiveness of machine learning algorithms in assessing personality
types. The predictive analysis will be based on a publicly available labelled dataset that contains users'
content from social media along with their personality type which is defined as the Myers Briggs Type
Indicator. Using natural language processing, the dataset will be pre-processed, and relevant content
features will be extracted for further processing. Then, suitable classification algorithms will be applied for the
purpose of personality assessment. The results of different machine learning algorithms together with their
advantages and disadvantages will be presented and explained.

• Keywords: Logistic Regression; Linear Support Vector Classifier; Multinomial Naive Bayes; Decision Tree Classifier; Random Forest Classifier
• Keywords (english): Logistic Regression; Linear Support Vector Classifier; Multinomial Naive Bayes; Decision Tree Classifier; Random Forest Classifier
• Language: english
• URN:NBN: urn:nbn:hr:168:322700
• Study programme: Title: Computing; Study programme type: university; Study level: undergraduate; Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2023-01-15 10:39:39