Sentiment Analysis in Twitter for Macedonian
Dame Jovanoski, Veno Pachovski, Preslav Nakov · 2015
We present work on sentiment analysis in Twitter for Macedonian. As this is pio-neering work for this combination of lan-guage and genre, we created suitable re-sources for training and evaluating a sys-tem for sentiment analysis of Macedonian tweets. In particular, we developed a cor-pus of tweets annotated with tweet-level sentiment polarity (positive, negative, and neutral), as well as with phrase-level sen-timent, which we made freely available for research purposes. We further boot-strapped several large-scale sentiment lex-icons for Macedonian, motivated by pre-vious work for English. The impact of several different pre-processing steps as well as of various features is shown in ex-periments that represent the first attempt to build a system for sentiment analysis in Twitter for the morphologically rich Macedonian language. Overall, our exper-imental 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. 1