Univerza na Primorskem Fakulteta za matematiko, naravoslovje in informacijske tehnologije
SI | EN

petek, 25. september 2020 Sead JAHIĆ: Handling tweet opinion by using weighted lexicon and lexicon-based approach

V ponedeljek, 28. septembra 2020, bo ob 16.00 uri prek spletnih orodij na daljavo izvedeno
predavanje v okviru PONEDELJKOVEGA SEMINARJA RAČUNALNIŠTVA IN INFORMATIKE
Oddelkov za Informacijske znanosti in tehnologije UP FAMNIT in UP IAM.

ČAS/PROSTOR: 28. september 2020 ob 16.00 na daljavo

-----------------------------------
PREDAVATELJ: Sead JAHIĆ
-----------------------------------

Sead Jahić is a teaching assistant at University of Primorska, Department of Information Sciences and Technologies.
After receiving High School diploma, he went to University of Tuzla (UNTZ), where in 2012 he graduated as Mathematician (Math Teacher).
His first working experience was teaching assistant at UNTZ, in the field of Applied Mathematics and Computer Science.
The main topic of his research is Sentiment Analysis of languages, where the focus is on developing overall sentiment analysis for West Balkan languages from the same West Slavic group.
The idea is to use different Machine Learning and Deep Learning techniques in sentiment analysis with those languages, and show huge connectivity between them.

-----------------------------------------------------------------------------------------------------------------------
NASLOV: Handling tweet opinion by using weighted lexicon and lexicon-based approach
-----------------------------------------------------------------------------------------------------------------------

POVZETEK:

In this seminar, we will present an introduction to creating a lexicon with weight values of words from our database. Given that the lexicon is made in such a way that the amount / number of positive and negative words affects the final outcome, we come to the conclusion that at some point, especially in neutral sentiment, we have words whose power can change the polarity of neutral to positive / negative. Therefore, when calculating sentiment, we relied on the weight lexicon created for a given amount of data.

---------------------------------------------------------------------------------------------------

Predavanje bo potekalo v angleškem jeziku prek spletnega orodja Zoom.
Do predavanja dostopate tako, da se povežete prek sledeče povezave:

https://us02web.zoom.us/j/297328207

Vabljeni!