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Title:Implementacija sistema za klasifikacijo novic o vrednostnih papirjih
Authors:ID Jakič, Jure (Author)
ID Kljajić Borštnar, Mirjana (Mentor) More about this mentor... New window
Files:.pdf MAG_Jakic_Jure_2021.pdf (1,84 MB)
MD5: C47E7BBBB5B57511E8C2FA655EDBBD4C
PID: 20.500.12556/dkum/40bf6290-c1b4-4e0c-8e0d-789f7c988c13
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FOV - Faculty of Organizational Sciences in Kranj
Abstract:Klasifikacija novic o podjetjih predstavlja časovno zelo dolgotrajen proces, saj je vsako novico potrebno prebrati in ji na podlagi vsebine določiti vsebinski pomen. Z razvojem metod za podatkovno rudarjenje lahko ta proces avtomatiziramo, s čimer novice razvrščamo v zanemarljivem času. V okviru magistrskega dela smo razvili sistem pridobivanja, prečiščevanja in klasifikacije novic. Novice smo pridobivali iz brezplačnih spletnih virov ter si ustvarili korpus besedil, ki smo jih najprej obdelali z orodjem Orange ter nato zgradili napovedne modele z uporabo različnih algoritmov. S pomočjo vizualizacij in matrike zamenjav smo prikazali kakovost napovednih modelov ter jih na podlagi njihove uspešnosti ovrednotili. S pomočjo ML.NET knjižnice smo na koncu razvili sistem avtomatske klasifikacije, ki novice glede na njihovo vsebino z 80 % natančnostjo klasificira v skupine.
Keywords:podatkovno rudarjenje, klasifikacija tekstov, trgovanje, novice, vrednostni papir
Place of publishing:Maribor
Year of publishing:2021
PID:20.500.12556/DKUM-78761 New window
COBISS.SI-ID:72527363 New window
NUK URN:URN:SI:UM:DK:WCFRHBEQ
Publication date in DKUM:09.08.2021
Views:723
Downloads:42
Metadata:XML RDF-CHPDL DC-XML DC-RDF
Categories:FOV
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:22.02.2021

Secondary language

Language:English
Title:Implementation of the securities news classification system
Abstract:Classification of news about companies represents a very time-consuming process, as each news has to be completely read to determine its content meaning. By using already developed data mining methods, we can automate this process and classify news in a negligible amount of time. During our master's thesis, we developed a system for obtaining, refining and classifying news. We obtained news from free online sources and created a corpus of texts. We first processed texts with the Orange tool, then we built predictive models by using different algorithms. Using visualizations and confusion matrices, we demonstrated the quality of predictive models, which were then evaluated based on their performance. We finally developed an automatic classification system by using ML.NET library, which is capable of classifying news into groups with 80 % accuracy.
Keywords:data mining, text classification, trading, news, security paper


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