Impact of Negation and AnA-Words on Overall Sentiment Value of the Text Written in the Bosnian Language

Abstract

In this manuscript, we present our efforts to develop an accurate sentiment analysis model for Bosnian-language tweets which incorporated three elements: negation cues, AnA-words (referring to maximizers, boosters, approximators, relative intensifiers, diminishers, and minimizers), and sentiment-labeled words from a lexicon. We used several machine-learning techniques, including SVM, Naive Bayes, RF, and CNN, with different input parameters, such as batch size, number of convolution layers, and type of convolution layers. In addition to these techniques, BOSentiment is used to provide an initial sentiment value for each tweet, which is then used as input for CNN. Our best-performing model, which combined BOSentiment and CNN with 256 filters and a size of 4×4, with a batch size of 10, achieved an accuracy of over 92%. Our results demonstrate the effectiveness of our approach in accurately classifying the sentiment of Bosnian tweets using machine-learning techniques, lexicons, and pre-trained models. This study makes a significant contribution to the field of sentiment analysis for under-researched languages such as Bosnian, and our approach could be extended to other languages and social media platforms to gain insight into public opinion.