Pregled bibliografske jedinice broj: 819168
Word occurrences and emotions in social media: case study on a twitter corpus
Word occurrences and emotions in social media: case study on a twitter corpus // Proceedings of the 39th International Convention on Information and Communication Technology, Electronics and Microelectronics MIPRO 2016 / Biljanović, Petar (ur.).
Rijeka: Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO, 2016. str. 1557-1560 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 819168 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Word occurrences and emotions in social media:
case study on a twitter corpus
Autori
Dunđer, Ivan ; Horvat, Marko ; Lugović, Sergej
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the 39th International Convention on Information and Communication Technology, Electronics and Microelectronics MIPRO 2016
/ Biljanović, Petar - Rijeka : Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO, 2016, 1557-1560
ISBN
978-953-233-087-8
Skup
39th International Convention on Information and Communication Technology, Electronics and Microelectronics MIPRO 2016
Mjesto i datum
Opatija, Hrvatska, 30.05.2016. - 03.06.2016
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
word occurrences ; emotion recognition ; social media analysis ; corpus analysis ; natural language processing ; Twitter
Sažetak
Twitter is currently the most popular tool for social interaction and real-time information exchange. Outreach and importance of individual accounts is measured by the number of their followers. The aim of this paper is to investigate the applicability and usefulness of corpora containing textual and visual information for the purpose of machine observation of Twitter activities. The results in the presented analytic research are based on a data set of more than 16000 tweets collected from 22 startup founders’ Twitter accounts with a large number of followers over a four-month period. Word usage in tweets was examined with natural language processing (NLP) techniques, applying word occurrence analyses and a manual qualitative evaluation of frequent words within the data set, primarily focusing on the distribution of words. Furthermore, profile pictures of Twitter accounts were collected in order to conduct a facial emotion analysis and emotion mining. Estimated basic emotional states were statistically compared with the number of tweets posted and the number of new followers gained during the observed timespan.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo, Informacijske i komunikacijske znanosti
POVEZANOST RADA
Ustanove:
Filozofski fakultet, Zagreb,
Tehničko veleučilište u Zagrebu