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Title:Primerjava algoritmov za analizo sentimenta v filmskih kritikah : magistrsko delo
Authors:ID Milutinović, Virdžinija (Author)
ID Bošković, Borko (Mentor) More about this mentor... New window
ID Brest, Janez (Comentor)
Files:.pdf MAG_Milutinovic_Virdzinija_2024.pdf (2,00 MB)
MD5: A8A73D9F6559B3FDCCB10C80422D33B5
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V magistrskem delu smo primerjali različne pristope za klasifikacijo sentimenta kritik filmov. Naš cilj je bil doseči čim višjo točnost pri klasifikaciji kritik. Uporabili smo algoritme, kot so metoda podpornih vektorjev, naključni gozdovi, naivni Bayes, odločitvena drevesa, k-najbližjih sosedov, logistična regresija in glasovanje. Rezultate smo merili z metriko točnosti. Ugotovili smo, da večji n-grami in algoritmi glasovanja dosegajo najboljšo točnost. Za najhitrejšo in najbolj točno klasifikacijo priporočamo algoritem glasovanja brez metode podpornih vektorjev, saj je bila ta najpočasnejša.
Keywords:analiza sentimenta, n-grami, algoritmi za klasifikacijo, točnost
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[V. Milutinović]
Year of publishing:2024
Number of pages:1 spletni vir (1 datoteka PDF (VII,65 f.))
PID:20.500.12556/DKUM-87011 New window
UDC:004.93.021(043.2)
COBISS.SI-ID:191510275 New window
Publication date in DKUM:01.03.2024
Views:449
Downloads:59
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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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:06.02.2024

Secondary language

Language:English
Title:Sentiment analysis on movie reviews: a comparative study of algorithms
Abstract:In the master’s thesis, we compared different approaches for sentiment classification of movie reviews. Our goal was to achieve the highest possible accuracy in classifying reviews. We used algorithms such as support vector machines, random forests, naive Bayes, decision trees, k-nearest neighbors, logistic regression and voting. The results were measured using the accuracy metric. We found that larger n-grams and voting algorithms achieve the best accuracy. For the fastest and most accurate classification, we recommend the voting algorithm without support vector machines, as the latter was the slowest of all algorithms.
Keywords:sentiment analysis, n-gram, classification algorithms, accuracy


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