This investigation investigates the influence of a variety of data augmentation techniques on sentiment analysis in low-resource languages, with a particular emphasis on Bulgarian, Croatian, Slovak, and Slovene. The following primary research topic is addressed: is it possible to improve sentiment analysis efficacy in low-resource languages through data augmentation? Our sub-questions look at how different augmentation methods affect performance, how effective WordNet-based augmentation is compared to other methods, and whether lemma-based augmentation techniques can be used, especially for Croatian sentiment tasks. The sentiment-labelled evaluations in the selected languages are included in our data sources, which were curated with additional annotations to standardise labels and mitigate ambiguities. Our findings show that techniques like replacing words with synonyms, masked language model (MLM)-based generation, and permuting and combining sentences can only make training datasets slightly bigger. However, they provide limited improvements in model accuracy for low-resource language sentiment classification. WordNet-based techniques, in particular, exhibit a marginally superior performance compared to other methods; however, they fail to substantially improve classification scores. From a practical perspective, this study emphasises that conventional augmentation techniques may require refinement to address the complex linguistic features that are inherent to low-resource languages, particularly in mixed-sentiment and context-rich instances. Theoretically, our results indicate that future research should concentrate on the development of augmentation strategies that introduce novel syntactic structures rather than solely relying on lexical variations, as current models may not effectively leverage synonymic or lemmatised data. These insights emphasise the nuanced requirements for meaningful data augmentation in low-resource linguistic settings and contribute to the advancement of sentiment analysis approaches.

Natural language processing for languages with limited resources is hampered by a lack of data. Using English as a hub language for such languages, cross-lingual sentiment analysis has been developed. The sheer quantity of English language resources raises questions about its status as the primary resource. This research aims to examine the impact on sentiment analysis of adding data from same-family versus distant-family languages. We analyze the performance using low-resource and high-resource data from the same language family (Slavic), investigate the effect of using a distant-family language (English) and report the results for both settings. Quantitative experiments using multi-task learning demonstrate that adding a large quantity of data from related and distant-family languages is advantageous for cross-lingual sentiment transfer.

This is the seventh publication in the series of biennial international conferences, The Future of Information Sciences (INFuture) organised by the Department of Information and Communication Sciences, Faculty of Humanities and Social Sciences, University of Zagreb. Since its beginnings twelve years ago, the INFuture conference has been providing a platform for discussing both theoretical and practical issues in information organization and information integration through the explorations of how developments in information and communication technology influence the future of the field of information sciences. Education and research in information sciences and its interdisciplinary scope and application is of particular interest to this conference which is aimed at researchers and professionals from the broad field of information and communication sciences and related professions. The title of this year's conference is INFuture2019: Knowledge in the Digital Age. The conference explores the influence the information and communication sciences have on the society as a whole.The INFuture2019 conference consists of 26 papers from 58 authors from nine countries -Austria, Croatia, Germany, Netherlands, Norway, Slovenia, South Korea, Sweden and United States.