info:eu-repo/semantics/conferenceProceedings Ljajić, Adela Prodanović, Nikola Medvecki, Darija Mitrović, Jelena Bašaragin, Bojana eng 2022 Information Society of Serbia - ISOS application/pdf 349933 bytes Proceedings of the 12th International Conference on Information Society and Technology All rights reserved Topic modeling technique on Covid19 tweets in Serbian https://unilib.phaidrabg.rs/o:1691 ISSN: 2738-1447 The COVID19 pandemic has brought health problems that concern individuals, the state, and the whole world. The information available on social networks, which were used more frequently and intensively during the pandemic than before, may contain hidden knowledge that can help to better address some problems and apply protective measures more adequately. Since the messages on Twitter are specific in their length, informal style, figurative speech, and frequent use of slang, this analysis requires the application of slightly different techniques than those classically applied to long, formal documents. To determine which topics appear in tweets related to vaccination, we apply state-of-the-art topic modeling techniques to determine which one is the most appropriate. This kind of research is meant to give us an insight into the opinions of the Twitter community on the phenomenon of vaccination and all related aspects. Comparing the results of the LDA with the topics obtained by manual annotation over the same set, we concluded that the LDA method provides a very good interpretation of the topics. Such data allow the analysis of sentiment, in this case pro- or anti-vaccination attitudes, and of specific groups of data and topics.