Opinion mining of text documents written in Macedonian language
This provides a tool for companies and individuals to analyze public opinion in Macedonian text data, though it is incremental as it applies existing methods to a new language.
The authors tackled opinion mining of Macedonian forum posts by classifying them as positive, negative, or neutral using machine learning, achieving 92% accuracy, which is comparable to human evaluation.
The ability to extract public opinion from web portals such as review sites, social networks and blogs will enable companies and individuals to form a view, an attitude and make decisions without having to do lengthy and costly researches and surveys. In this paper machine learning techniques are used for determining the polarity of forum posts on kajgana which are written in Macedonian language. The posts are classified as being positive, negative or neutral. We test different feature metrics and classifiers and provide detailed evaluation of their participation in improving the overall performance on a manually generated dataset. By achieving 92% accuracy, we show that the performance of systems for automated opinion mining is comparable to a human evaluator, thus making it a viable option for text data analysis. Finally, we present a few statistics derived from the forum posts using the developed system.