prikaz prve stranice dokumenta Analiza sentimenta pomoću Naive Bayes algoritma
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undergraduate thesis
Analiza sentimenta pomoću Naive Bayes algoritma
2020. urn:nbn:hr:166:244623
Koprčina, Josip
University of Split
Faculty of Science
Department of Informatics

Cite this document

Koprčina, J. (2020). Analiza sentimenta pomoću Naive Bayes algoritma (Undergraduate thesis). Split: University of Split, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:166:244623

Koprčina, Josip. "Analiza sentimenta pomoću Naive Bayes algoritma." Undergraduate thesis, University of Split, Faculty of Science, 2020. https://urn.nsk.hr/urn:nbn:hr:166:244623

Koprčina, Josip. "Analiza sentimenta pomoću Naive Bayes algoritma." Undergraduate thesis, University of Split, Faculty of Science, 2020. https://urn.nsk.hr/urn:nbn:hr:166:244623

Koprčina, J. (2020). 'Analiza sentimenta pomoću Naive Bayes algoritma', Undergraduate thesis, University of Split, Faculty of Science, accessed 17 November 2024, https://urn.nsk.hr/urn:nbn:hr:166:244623

Koprčina J. Analiza sentimenta pomoću Naive Bayes algoritma [Undergraduate thesis]. Split: University of Split, Faculty of Science; 2020 [cited 2024 November 17] Available at: https://urn.nsk.hr/urn:nbn:hr:166:244623

J. Koprčina, "Analiza sentimenta pomoću Naive Bayes algoritma", Undergraduate thesis, University of Split, Faculty of Science, Split, 2020. Available at: https://urn.nsk.hr/urn:nbn:hr:166:244623

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Metadata
TitleAnaliza sentimenta pomoću Naive Bayes algoritma
Title (english)Sentiment analysis using the Naive Bayes algorithm
AuthorJosip Koprčina
MentorSaša Mladenović (mentor)
Committee memberSaša Mladenović (predsjednik povjerenstva)
Committee memberGoran Zaharija (član povjerenstva)
Committee memberDivna Krpan (član povjerenstva)
GranterUniversity of Split
Faculty of Science
(Department of Informatics)
Split
Defense date and country2020-07-29, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Analiza sentimenta je proces klasifikacije podataka iz teksta u predefinirane skupine. Dijeli se na više tipova od kojih je najčešći Fine-grained tip u kojem se traži polaritet određenog objekta ili atributa. Kako bi funkcionirao koristi statističke modele i modele umjetne inteligencije. Koristi se u više svrha, ali je najpopularnija u ekonomiji, gdje firme prate osjećaje i mišljenje korisnika o njihovom proizvodu i njegovim atributima. U ovom radu su opisani neki od najkorištenijih modela koji se primjenjuju pri analizi sentimenta, ali je naglasak stavljen na Naive Bayes algoritam. Pokazani su problemi pri korištenju analize sentimenta i dan je primjer kako napraviti jednostavan program za pronalazak mišljenja korisnika o određenom pojmu na socijalnoj platformi Twitter.
Abstract (english)
Sentiment analysis is the proces of classifing text into predefined groups. There are many types but the most common is Fine-grained sentiment analysis, where we look for the polarity of user feelings towards a certain object or it's atributes. To function, it uses various statistical and artificial intelligence models. It is used in many fields but the most common use is by companies to follow what users feel about their product. In this work we explain the most common models used in sentiment analysis but we put the focus on the Naive Bayes algorithm. We show the problems with using sentiment analyis and give an example on how to make a simple program for finding out what people on Twitter feel about a certain word.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:166:244623
Study programmeTitle: Computer Science
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/prvostupnica (baccalaureus/baccalaurea) informatike (sveučilišni/a prvostupnik/prvostupnica (baccalaureus/baccalaurea) informatike)
Type of resourceText
File originBorn digital
Access conditionsAccess restricted to students and staff of home institution
Terms of useLicence In copyright icon
Created on2021-01-14 11:44:33