# Transferring Sentiment Cross-Lingually within and across Same-Family Languages
## 5 Data  
Our supervised resources include datasets in eight distinct languages, seven of which are official EU languages. We considered English to be the source language for all pairs of languages. Bulgarian, Croatian, Czech, Polish, Slovak, and Slovene are the target languages. A single dataset was selected for each language in the study. In Table 1, we present the sizes of the datasets' training, development, and test splits.  
_Sentiment Analysis Datasets_  
* Bulgarian: The Cinexio [51] dataset is composed of film reviews with 11-point star ratings: 0 (negative), 0.5, 1,... 4.5, 5 (positive). Other meta-features included in the dataset are film length, director, actors, genre, country, and various scores.
* Croatian: Pauza [68] contains restaurant reviews from Pauza.hr4, the largest food ordering website in Croatia. Each review is assigned an opinion rating ranging from 0.5 (worst) to 6 (best). User-assigned ratings are the benchmark for labels. The dataset also contains opinionated aspects.
* Czech: The CSFD [108] dataset was influenced by [PERSON] et al. [109]. It includes film reviews from the Czech Movie Database ([[http://www.csfd.cz](http://www.csfd.cz)]([http://www.csfd.cz](http://www.csfd.cz)) accessed on 10 September 2023). Every review is classified as either positive, neutral, or negative.
* English: The Multilingual Amazon Reviews Corpus (MARC) is a large collection of Amazon reviews [110]. The corpus contains reviews written in Chinese, English, Japanese, German, French, and Spanish. Each review is assigned a maximum of five stars. Each record contains the review text, the title, the star rating, and product-related metadata.
* Polish: The Wroclaw Corpus of Consumer Reviews Sentiment [77] is a multi-domain dataset of Polish reviews from the domains of schools, medicine, hotels, and products. The texts have been annotated at both the sentence level and the text body level. The reviews are labeled as follows: [+m] represents a strong positive; [+s] represents a weak positive; [-m] represents a strong negative; [-s] represents a weak negative; [amb] represents ambiguity; and [0] represents neutrality.
* Russian: The ROMIP-12 dataset [80] is composed of news-based opinions, which are excerpts of the direct and indirect speech published in news articles. Politics,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Language** & **Dataset** & **Train** & **Validate** & **Test** \\ \hline Bulgarian & Cinexio & 5520 & 614 & 682 \\ Croatian & Pauza & 2277 & & 1033 \\ Czech & CSFD & 63,966 & 13,707 & 13,707 \\ English & MARC & 200,000 & 5000 & 5000 \\ Polish & all2 & 28,581 & 3572 & 3572 \\ Russian & ROIMP 2012 & 4000 & 260 & 5500 \\ Slovak & Reviews3 & 3834 & 661 & 1235 \\ Slovene & KKS & 3977 & 200 & 600 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution of sentiment analysis datasets.  
economics, sports, and the arts are just some of the diverse subject areas covered. This dataset contains speech classified as positive, neutral, or negative.
* Slovak: The Review3 [111] is composed of customer evaluations of a variety of services. The dataset is categorized using the 1-3 and 1-5 scales.
* Slovene: The Opinion corpus of Slovene web commentaries KKS 1.001 [90] includes web commentaries on various topics (business, politics, sports, etc.) from four Slovene web portals (RtvSlo, 24 ur, Finance, and Reporter). Each instance within the dataset is tagged with one of the three labels (negative, neutral, or positive).  
Language models: XLM-RoBERTa is a language model that has been pre-trained in 100 different languages.We chose this model as the foundation for the fine-tuning process because all of the target languages were present during the model's pre-training.