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Pregled bibliografske jedinice broj: 1066686

Predicting Dependency of Approval Rating Change from Twitter Activity and Sentiment Analysis

Grgić, Demijan; Karaula, Mislav; Bagic Babac, Marina; Podobnik, Vedran
Predicting Dependency of Approval Rating Change from Twitter Activity and Sentiment Analysis // Agents and Multi-Agent Systems: Technologies and Applications 2020 / Jezic G ; Chen-Burger J ; Kusek M ; Sperka R ; Howlett R ; Jain L (ur.).
Singapur: Springer, 2020. str. 103-112 doi:10.1007/978-981-15-5764-4_10 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), ostalo)

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Naslov
Predicting Dependency of Approval Rating Change from Twitter Activity and Sentiment Analysis

Autori
Grgić, Demijan ; Karaula, Mislav ; Bagic Babac, Marina ; Podobnik, Vedran

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

Izvornik
Agents and Multi-Agent Systems: Technologies and Applications 2020 / Jezic G ; Chen-Burger J ; Kusek M ; Sperka R ; Howlett R ; Jain L - Singapur : Springer, 2020, 103-112

ISBN
978-981-15-5764-4

Skup
14th International KES Conference on Agents and Multi-Agent Systems: Technologies and Applications (KES-AMSTA 2020)

Mjesto i datum
Split, Hrvatska, 17.06.2020. - 19.06.2020

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Twitter ; NRC sentiment ; Emotional sentiment ; Supervised learning ; Social networks ; Approval rating

Sažetak
In recent years, multi-agent systems have been augmented with alternative social network data like Twitter for preforming target inference. To this extent, public figures tracked by agent system usually have key social value in social networks. This social value can additionally drift in public approval based on social network communication. To test the connection in public approval and social communication, we perform analysis of presidential Twitter account activity during the two-year period 2017–2018. Sentiment analysis was used on the processed tweets in order to test such a data set’s predictability in gaining an insight into a 7, 14 and 21 days (1, 2 and 3 weeks) significant presidential job approval rating change. To this extent, five different supervised machine learning algorithms are used: Random Forest, Xgboost, AdaBoost, AdaBag and ExtraTrees. Results indicate that voter approval rating has slight future predictability based on Twitter activity and emotional sentiment analysis possibly indicating consistency with the human nature of positive news and outcomes resonating with people for a much shorter period than negative ones.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Interdisciplinarne tehničke znanosti, Interdisciplinarne društvene znanosti



POVEZANOST RADA

Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb

Profili:

Avatar Url Vedran Podobnik (autor)

Avatar Url Marina Bagić Babac (autor)

Avatar Url Demijan Grgić (autor)

Poveznice na cjeloviti tekst rada:

doi link.springer.com

Citiraj ovu publikaciju:

Grgić, Demijan; Karaula, Mislav; Bagic Babac, Marina; Podobnik, Vedran
Predicting Dependency of Approval Rating Change from Twitter Activity and Sentiment Analysis // Agents and Multi-Agent Systems: Technologies and Applications 2020 / Jezic G ; Chen-Burger J ; Kusek M ; Sperka R ; Howlett R ; Jain L (ur.).
Singapur: Springer, 2020. str. 103-112 doi:10.1007/978-981-15-5764-4_10 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), ostalo)
Grgić, D., Karaula, M., Bagic Babac, M. & Podobnik, V. (2020) Predicting Dependency of Approval Rating Change from Twitter Activity and Sentiment Analysis. U: Jezic G, Chen-Burger J, Kusek M, Sperka R, Howlett R & Jain L (ur.)Agents and Multi-Agent Systems: Technologies and Applications 2020 doi:10.1007/978-981-15-5764-4_10.
@article{article, author = {Grgi\'{c}, Demijan and Karaula, Mislav and Bagic Babac, Marina and Podobnik, Vedran}, year = {2020}, pages = {103-112}, DOI = {10.1007/978-981-15-5764-4\_10}, keywords = {Twitter, NRC sentiment, Emotional sentiment, Supervised learning, Social networks, Approval rating}, doi = {10.1007/978-981-15-5764-4\_10}, isbn = {978-981-15-5764-4}, title = {Predicting Dependency of Approval Rating Change from Twitter Activity and Sentiment Analysis}, keyword = {Twitter, NRC sentiment, Emotional sentiment, Supervised learning, Social networks, Approval rating}, publisher = {Springer}, publisherplace = {Split, Hrvatska} }
@article{article, author = {Grgi\'{c}, Demijan and Karaula, Mislav and Bagic Babac, Marina and Podobnik, Vedran}, year = {2020}, pages = {103-112}, DOI = {10.1007/978-981-15-5764-4\_10}, keywords = {Twitter, NRC sentiment, Emotional sentiment, Supervised learning, Social networks, Approval rating}, doi = {10.1007/978-981-15-5764-4\_10}, isbn = {978-981-15-5764-4}, title = {Predicting Dependency of Approval Rating Change from Twitter Activity and Sentiment Analysis}, keyword = {Twitter, NRC sentiment, Emotional sentiment, Supervised learning, Social networks, Approval rating}, publisher = {Springer}, publisherplace = {Split, Hrvatska} }

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