###### 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 \(4\times 4\), 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. the Bosnian language; lexicon; negation cues; AnA-words; intensifiers; diminishers; sentiment analysis; neural network; convolution neural network +
Footnote †: journal: Medical  
in media and particularly on the Internet. In terms of foreign names and words, Serbian typically employs phonetic transcription, whereas the Croatian and the Bosnian standards generally opt for transliteration. It is important to note that the pronunciation known as Ikavian, which deviates from the standard, is limited to specific dialectal usage in regions such as Dalmatia, Lika, Istria, central Bosnia (between the Vrbas and Bosna rivers), Western Herzegovina, and Bosanska Krajina. Regarding the term \"child\", illustrated by the Common Slavic word _dite_, the three principal pronunciations are as follows: _dite_ in the Ikavian pronunciation, _dijet_ in the Jekavian pronunciation, and _dete_ in the Ekavian pronunciation. It should be noted that Bosnian exclusively accepts the Jekavian pronunciation. Moreover, the Bosnian language, unlike the other mentioned languages, distinguishes itself by incorporating a higher number of borrowed words, particularly from the Turkish language. This influence can be attributed to historical interactions between the Ottoman Empire and the region, resulting in the adoption of numerous Turkish terms into the Bosnian vocabulary. These borrowed words contribute to the linguistic richness and cultural diversity of the Bosnian language. With approximately 2.5 million speakers in Europe, including 1.87 million individuals in Bosnia and Herzegovina alone, the Bosnian language constitutes the mother tongue for a considerable portion of the population [2; 3]. Additionally, it finds usage among around 150,000 people in Western Europe and North America, as well as 100,000 to 200,000 individuals in Turkey [2]. In recent years, the explosive growth of social media platforms has resulted in an enormous amount of user-generated content, making sentiment analysis an increasingly important field of research in natural language processing (NLP) [4; 5; 6]. Transformers have revolutionized the field of natural language processing (NLP). The Transformer model, introduced by [PERSON] et al. [7] in 2017, uses self-attention mechanisms to capture contextual information and dependencies between words. One prominent transformer-based model is the BERT (Bidirectional Encoder Representations from Transformers), which has achieved state-of-the-art results in various NLP tasks, including sentiment analysis [8]. Sentiment analysis aims to automatically determine the sentiment expressed in text, allowing for the extraction of valuable insights from user opinions, emotions, and attitudes [5; 9; 10].