Dataset

The SentiComments.SR dataset includes the following three corpora of short texts annotated for the task of sentiment analysis:
The main SentiComments.SR corpus, consisting of 3490 movie-related comments;
The movie verification corpus, consisting of 464 movie-related comments;
The book verification corpus, consisting of 173 book-related comments.
Six sentiment labels were used in dataset annotation: +1, -1, +M, -M, +NS, and -NS, with the addition of an ‘s’ label suffix denoting the presence of sarcasm. The main corpus was annotated by two annotators working together, and therefore contains a single, unified sentiment label for each comment. The verification corpora were used to evaluate the quality, efficiency, and cost-effectiveness of the annotation framework, which is why they contain separate sentiment labels for six annotators. The construction of this dataset is described in the 2020 PLoS ONE paper.

Rapid Integrated Assessment (RIA) is a national policy document evaluation mechanism developed by the UNDP to help countries assess their readiness for the implementation of UN Sustainable Development Goals (SDG). The created model automates the RIA procedure for documents written in Serbian and is based on an earlier IBM approach developed for English. The model works by searching the documents for sentences / paragraphs that are a semantic match for one the SDG targets. The model repository also contains the Serbian national policy documents, as well as their stemmed versions. Further information can be found in the LT4All paper.

SETimes.SR reference training corpus of Serbian consists of 87 thousand tokens or close to four thousand sentences in Serbian, gathered from the (now defunct) Southeast European Times news portal. Each news story is treated as a separate document and is segmented into sentences and tokens. The entire corpus is annotated on the level of lemmas and parts of speech, morphosyntax, syntactic dependencies, and named entities. The construction of this corpus is described in a JT-DH 2018 paper.

The Serbian Semantic Textual Similarity News Corpus – STS.news.sr (ISLRN 146-979-597-345-4) consists of 1192 pairs of sentences in Serbian gathered from news sources on the web. Each sentence pair was manually annotated with fine-grained semantic similarity scores on the 0–5 scale. The final scores were obtained by averaging the individual scores of five annotators. The construction of this corpus is described in the LREC 2018 paper.

The Serbian Movie Review Dataset (SerbMR) collection consists of three movie review datasets in Serbian which were constructed for the task of sentiment analysis:
Collected movie reviews in Serbian (ISLRN 252-457-966-231-5) – an unbalanced collection of 4725 movie reviews in Serbian.
SerbMR-2C – The Serbian Movie Review Dataset (2 Classes) (ISLRN 016-049-192-514-1) – a two-class balanced sentiment analysis dataset containing 1682 movie reviews in Serbian (841 positive and 841 negative reviews).
SerbMR-3C – The Serbian Movie Review Dataset (3 Classes) (ISLRN 229-533-271-984-0) – a three-class balanced sentiment analysis dataset containing 2523 movie reviews in Serbian (841 positive, 841 neutral, and 841 negative reviews).
The construction of this dataset collection is described in the LREC 2016 paper.

The Serbian Paraphrase Corpus – paraphrase.sr (ISLRN 192-200-046-033-9) consists of 1194 pairs of sentences gathered from news sources on the web. Each sentence pair was manually annotated with a binary similarity score that indicates whether the sentences in the pair are semantically similar enough to be considered close paraphrases. The construction of this corpus is described in the 2011 TELFOR paper and the 2013 Decision Support Systems paper.