Naslov
Propaganda Detection Using Sentiment Aware Ensemble Deep Learning

Autori
Polonijo, Bruno ; Šuman, Sabrina ; Šimac, Ivan

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
MIPRO 2021 44th International Convention / Skala, Karolj - Opatija : Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO, 2021, 225-230

Skup
44th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO 2021)

Mjesto i datum
Opatija, Hrvatska, 27.09.2021. - 01.10.2021

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
propaganda ; sentiment aware word representation ; deep learning ; Word2Vec

Sažetak
In today's highly globalized world with vast information transfers, it is increasingly difficult to distinguish valid information from attempts to manipulate human attitude through propaganda, which poses a growing threat due to its spread and sophistication. This paper proposes a deep learning method in order to combine sentiment scores with traditional Word2Vec vectors which result in a sentiment aware representation containing semantic and emotional information, which, when used together, result in a more accurate propaganda classification model. The Word2Vec vector method is a useful tool used to recognize the semantic meaning of words and their structures in natural language processing, i.e., their emotional classification, and thus to detect propaganda. An emotional dictionary built into VADER's sentiment analysis results in a text sentiment score representing emotional information. This method preserves the flexibility of the Word2Vec vector by combining it with an output of sentiment analysis. Tests conducted using a Word2Vec model without sentiment data and using sentiment data with standard deep learning methods for propaganda detection show that this hybrid approach increases propaganda classification accuracy.

Izvorni jezik
Engleski

Znanstvena područja
Interdisciplinarne tehničke znanosti, Informacijske i komunikacijske znanosti

POVEZANOST RADA