Analiza podataka sa socijalnih mreža podržana strojnim učenjem

undergraduate thesis

• Title (english): Analysis of data from social networks supported by machine learning
• Author: Arijana Bikić
• Mentor: Tomislav Stipančić (mentor)
• Granter: University of Zagreb; Faculty of Mechanical Engineering and Naval Architecture; Zagreb
• Defense date and country: 2024-09-25, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Mechanical Engineering

Abstract

Sažetak ovog završnog rada istražuje primjenu analize sentimenta kroz strojno učenje na podacima s Twitter-a. Analiza sentimenta je proces analiziranja i prepoznavanja emocija ili stavova iz teksta, a strojno učenje je grana umjetne inteligencije koja omogućuje računalima da nauče iz podataka i izvode zadatke bez eksplicitnog programiranja. Ovaj rad istražuje kako se modeli strojnog učenja, posebno Naivni Bayesov klasifikator, mogu koristiti za analizu sentimenta na Twitter-ovim podacima. Kroz eksperimente i evaluaciju, analizira se učinkovitost ovih modela u prepoznavanju pozitivnih i negativnih sentimenta u tweetovima te pruža uvid u izazove i mogućnosti ovog pristupa.

Abstract (english)

The summary of this thesis explores the application of sentiment analysis through machine learning on Twitter data. Sentiment analysis is the process of analyzing and recognizing emotions or attitudes from text, and machine learning is a branch of artificial intelligence that allows computers to learn from data and perform tasks without explicit programming. This paper explores how machine learning models, specifically the Naive Bayes classifier, can be used for sentiment analysis on Twitter data. Through experiments and evaluation, the effectiveness of these models in recognizing positive and negative sentiments in tweets is analyzed and provides insight into the challenges and opportunities of this approach.

• Keywords: NLP; Python; Sentiment Analysis
• Keywords (english): NLP; Python; Sentiment Analysis
• Language: croatian
• URN:NBN: urn:nbn:hr:235:298964
• Study programme: Title: Mechanical Engineering; Study programme type: university; Study level: undergraduate; Academic / professional title: sveučilišni/a prvostupnik/prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka strojarstva (sveučilišni/a prvostupnik/prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka strojarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Open access
• Created on: 2024-09-16 14:15:22