# Sentiment Analysis in Twitter for Macedonian

arXiv: https://arxiv.org/abs/2109.13725 · HTML: https://mlsanity.com/paper/2109.13725/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 47 references that are in the map.
- First posted on arXiv 27 Sep 2021.

## Builds on (most cited first, 8 of 8)

- [NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets](https://mlsanity.com/paper/1308.6242/) (2013)
- [SemEval-2013 Task 2: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.06806/) (2019)
- [SemEval-2014 Task 9: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.02990/) (2019)
- [SemEval-2015 Task 10: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.02387/) (2019)
- [Global Thread-Level Inference for Comment Classification in Community Question Answering](https://mlsanity.com/paper/1911.08755/) (2019)
- [Exposing Paid Opinion Manipulation Trolls](https://mlsanity.com/paper/2109.13726/) (2021)
- [Language-Independent Sentiment Analysis Using Subjectivity and Positional Information](https://mlsanity.com/paper/1911.12544/) (2019)
- [Opinion mining of text documents written in Macedonian language](https://mlsanity.com/paper/1411.4472/) (2014)

## Built on by (most cited first, 1 of 1)

- [NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis](https://mlsanity.com/paper/2201.08277/) (2022)

## Abstract (arXiv)

We present work on sentiment analysis in Twitter for Macedonian. As this is pioneering work for this combination of language and genre, we created suitable resources for training and evaluating a system for sentiment analysis of Macedonian tweets. In particular, we developed a corpus of tweets annotated with tweet-level sentiment polarity (positive, negative, and neutral), as well as with phrase-level sentiment, which we made freely available for research purposes. We further bootstrapped several large-scale sentiment lexicons for Macedonian, motivated by previous work for English. The impact of several different pre-processing steps as well as of various features is shown in experiments that represent the first attempt to build a system for sentiment analysis in Twitter for the morphologically rich Macedonian language. Overall, our experimental results show an F1-score of 92.16, which is very strong and is on par with the best results for English, which were achieved in recent SemEval competitions.