Primjena modela dubokog učenja na analizu sentimenta izraza hrvatskoga jezika

master's thesis

• Title (english): Using Deep Learning for Sentiment Analysis of Expressions in Croatian Language
• Author: Siniša Biđin
• Mentor: Jan Šnajder (mentor)
• Committee member: Jan Šnajder (predsjednik povjerenstva)
• Committee member: Siniša Šegvić (član povjerenstva)
• Committee member: Nikola Bogunović (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2014-07-07, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

Uobičajeni postupci analize sentimenta temelje se na rječniku apriornog sentimenta. Problem predstavlja modeliranje sentimenta višerječnih izraza poput ``poprilično dobar'' ili ``nimalo loš'', ali i većih jezičnih jedinica. Opisan je i implementiran postupak učenja reprezentacija riječi prema metodi Colloberta i dr.~(2011) te postupak analize sentimenta višerječnih izraza hrvatskoga jezika modelom zasnovanom na rekurzivnoj neuronskoj mreži prema radu Sochera i dr.~(2012). Navedeni su rezultati učenja reprezentacija
riječi na temelju dva različita korpusa i rezultati evaluacije modela za analizu sentimenta nad tri različita skupa za učenje.

Abstract (english)

Methods of sentiment analysis are usually based upon a sentiment-labeled lexicon. Problems arise when trying to model the sentiment of multi-word phrases such as ``pretty good'' or ``not bad'', but also of entire sentences. A word-representation training method based on Collobert et al.~(2011) is defined and implemented, as is a method for sentiment analysis of multi-word phrases written in Croatian using a model based on recursive neural networks according to Socher et al.~(2012). We state the results of word-representation training on two different corpora and the results of evaluating the sentiment analysis model on three different training sets.

• Keywords: obrada prirodnog jezika; duboko učenje; učenje reprezentacija riječi; analiza sentimenta; hrvatski jezik
• Keywords (english): natural language processing; deep learning; word representation pretraining; sentiment analysis; Croatian language
• Language: croatian
• URN:NBN: urn:nbn:hr:168:760886
• Study programme: Title: Computing; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2018-02-06 07:53:31