prikaz prve stranice dokumenta Analiza sentimenta u recenzijama proizvoda
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undergraduate thesis
Analiza sentimenta u recenzijama proizvoda
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. urn:nbn:hr:168:281357
Palić, Niko
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Palić, N. (2019). Analiza sentimenta u recenzijama proizvoda (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:281357

Palić, Niko. "Analiza sentimenta u recenzijama proizvoda." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:281357

Palić, Niko. "Analiza sentimenta u recenzijama proizvoda." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:281357

Palić, N. (2019). 'Analiza sentimenta u recenzijama proizvoda', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 18 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:281357

Palić N. Analiza sentimenta u recenzijama proizvoda [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2019 [cited 2024 November 18] Available at: https://urn.nsk.hr/urn:nbn:hr:168:281357

N. Palić, "Analiza sentimenta u recenzijama proizvoda", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:168:281357

Metadata
TitleAnaliza sentimenta u recenzijama proizvoda
Title (english)Product Review Sentiment Analysis
AuthorNiko Palić
MentorJan Šnajder (mentor)
Committee memberJan Šnajder (predsjednik povjerenstva)
Committee memberDomagoj Alagić (član povjerenstva)
Committee memberZoran Medić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2019-07-12, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Cilj ovog rada bio je izgraditi model strojnog učenja koji može što preciznije moguće predvidjeti broj zvjezdica (rejting) neke recenzije proizvoda, koriste ́ci tekst recenzije. Recenzije su dane u Amazonovom skupu podataka. Svaka recenzija ima tijelo, naslov i broj zvjezdica. Pripremanje skupa podataka uključivalo je tokenizaciju, micanje beskorisnih riječi i reduciranje riječi u korijen. Korišteni modeli su Gaussov naivni Bayesov klasifikator i sekvencijalna neuronska mreža. Trenirani su na različitim kombinacijama značajki (Word2Vec, mjera polarnosti, mjera subjektivnosti, brojač posebnih znakova i brojač riječi napisanih velikim slovima). Iz rezultata testiranja modela na prije neviđenim podacima, može se zaključiti da neuronske mreže djeluju bolje na ovakvom problemu. Kombiniranje svih značajki dalo je otprilike jednaku preciznost kao i korištenje samog Word2Vec-a. Najbolja mjera preciznosti i R2 mjera bile su umjerene, ali daleko veće od mjera osnovnog modela.
Abstract (english)
The task for this thesis was to build machine learning models to, as accurately as possible, predict star ratings of product reviews, by using only text of those reviews. The reviews are supplied in Amazon's product review dataset. Each review consisted of a title, body and accompanying star rating. Preprocessing of the dataset included tokenization, removing of stopwords and stemming. The models used were the gaussian naive Bayes classifier and the sequential neural network. They were trained of different combination of features (Word2Vec, polarity score, subjectivity score, special sign counter, and uppercased words counter). From the results of testing the models on unseen data, it can be concluded that neural networks work better for this task. Combining all the features gave approximately the same accuracy as only using Word2Vec. The best classification accuracy and R2 score achieved were modest, but greater than those of the baseline model.
Keywords
Keywords (english)
Languageenglish
URN:NBNurn:nbn:hr:168:281357
Study programmeTitle: 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 resourceText
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Created on2020-01-07 20:54:36