# Towards Reliable Baselines for Document-Level Sentiment Analysis in the Czech and Slovak Languages
## 6 Conclusions and Future Work  
In this article we replicated several experiments from [PERSON] et al. (2016), which represents the most detailed and comprehensive exploration of supervised machine learning, feature selection, and data pre-processing methods for sentiment analysis of Czech social media undertaken to date. Due to its comprehensiveness, it often serves as a baseline for other sentiment analysis studies not only in Czech, but thanks to their close similarity, also in Slovak. Moreover, [PERSON] et al. (2016) also collected three valuable datasets of opinionated online texts in Czech--Facebook comments, product reviews from Mall.cz, and movie reviews from csfd.cz--and made them freely available to other researchers at [[http://liks.fav.zcu.cz/sentiment/](http://liks.fav.zcu.cz/sentiment/)]([http://liks.fav.zcu.cz/sentiment/](http://liks.fav.zcu.cz/sentiment/)) (accessed on 17 October 2022). The size of these datasets outstripped earlier such efforts by an order of magnitude, and they have in fact remained a popular and almost unrivalled data resource among researchers to this day. We believe that our replication experiments and the resulting error corrections elaborated in Section 4 will help to make this baseline even more trustworthy and widely used. In order to reap maximum benefit from it, we would recommend the concerned researchers to also heed methodological considerations that we reiterated in Section 5. Regarding future work, arguably the best possible use of a reliable baseline for sentiment analysis in Czech and Slovak would be to quickly reduce the gap separating these under-resourced languages from the vanguard of sentiment analysis research taking place mostly in English. Since sentiment analysis in its most advanced aspect-based form is, in the words of [PERSON] (2017), \"a mini-version of the full NLP or a special case of the full NLP... [because] every subproblem of NLP is also a subproblem of sentiment analysis, and vice versa... [including] lexical semantics, coreference resolution, word sense disambiguation, discourse analysis, information extraction, and semantic analysis\", any significant progress in sentiment analysis would naturally translate into a significant progress for NLP in general. Although sentiment analysis is traditionally associated only with specific kindsof data, such as Facebook comments and online product or movie reviews, its potential uses extend far beyond this narrow province. Thus, for example, in one of our national research projects dealing with composable scientific workflows and data processing pipelines (APVV-20-0571 iControl), we contemplate its application to end-user feedback concerning the utility of individual workflow and pipeline elements or modules, to enable artificial intelligence to decide which of several available implementations of a given algorithm to choose for the best results. Data curation, J.M. ; Formal analysis, P.K., M.K. and L.S. ; Methodology, P.K. and M.K. ; Project administration, L.H. ; Resources, J.M. ; Software, J.M. and P.K. ; Supervision, L.H. ; Validation, P.K., M.K. and L.S. ; Writing--original draft, M.K. ; Writing--review and editing, J.M., M.K. and P.K. All authors have read and agreed to the published version of the manuscript. This work was supported by the Slovak Research and Development Agency under the Contract no. APVV-20-0571 (iControl) and by the projects VEGA Nos. 2/0155/19 and 2/0125/20. We have used the three datasets made public by [PERSON] et al. [50] and available at [[https://liks.fav.zcu.cz/sentiment/](https://liks.fav.zcu.cz/sentiment/)]([https://liks.fav.zcu.cz/sentiment/](https://liks.fav.zcu.cz/sentiment/)) (accessed on 17 October 2022). Prospective users should check for the presence of duplicates before analyzing these datasets. We would like to express our thanks to [PERSON] et al.