EMBEDDIA hackathon report: Automatic sentiment and viewpoint analysis of Slovenian news corpus on the topic of LGBTIQ+ Matej Martinc Nina Perger Andrazˇ Pelicon Jozefˇ Stefan Institute Faculty of Social Sciences Jozefˇ Stefan Institute Jamova 39, Ljubljana Kardeljeva plosˇcadˇ 5, Ljubljana Jamova 39, Ljubljana
[email protected] [email protected] andraz.pelicon@@ijs.si Matej Ulcarˇ Andreja Vezovnik Senja Pollak Faculty of Computer Science Faculty of Social Sciences Jozefˇ Stefan Institute Vecnaˇ pot 113, Ljubljana Kardeljeva plosˇcadˇ 5, Ljubljana Jamova 39, Ljubljana
[email protected] [email protected] [email protected] Abstract public objection (Kania, 2020) and church – state opposition (Paterson and Coffey-Glover, 2018). We conduct automatic sentiment and view- The related work also shows that the differences point analysis of the newly created Slovenian between ”liberal” and ”conservative” arguments news corpus containing articles related to the are not emphasised, mostly because both sides refer topic of LGBTIQ+ by employing the state-of- the-art news sentiment classifier and a system to each other’s arguments, if only to negate them; for semantic change detection. The focus is yet, political orientation can be identified through on the differences in reporting between quality the tone of the article (Zheng and Chan, 2020). news media with long tradition and news me- When it comes to methods employed for auto- dia with financial and political connections to matic analysis of the LGBTIQ+ topic, most re- SDS, a Slovene right-wing political party. The cent approaches rely on embeddings. Hamilton results suggest that political affiliation of the et al.(2016) employed embeddings to research media can affect the sentiment distribution of how words (among them also word gay) change articles and the framing of specific LGBTIQ+ specific topics, such as same-sex marriage.