## 3 Methodology  
In this study, we aimed to conduct a comparison of sentiment analysis models while taking into account the impact of negation and terms from the AnA-words list. We achieved this by using two models:  
* The **BoSA model**, which considers the presence of negation and lexicon terms in the sentence (see Figure 1). Moreover, if there is no negation in a tweet, and no lexicon terms, the final sentiment value is 0 (neutral). However, if there is no negation and a positive word from the lexicon is present, the final sentiment value is 1 (positive); conversely, if a negative word from the lexicon is present the sentimental value is \(-1\) (negative). In the case where both positive and negative words from the lexicon are present simultaneously, the sentiment value is calculated using the following Equation (1). Furthermore, if there is only negation without any words carrying sentiment, the final sentiment value of the tweet is 0. However, if both negation and a positive word are present, the sentiment value is reversed, resulting in a value of \(-1\). Conversely, if a negative word is present alongside negation, the sentiment value is 1. Finally, if both negation and both positive and negative words are present, the sentiment value of the tweet is determined by which word is closer to the negation. In this case, if the positive word is closer, the sentiment value is \(-1\); otherwise, it is 1. * The **BOSentiment model**, which examines the position of negation and AnA-word terms in relation to all other words in the text (see Figure 2). Moreover, the BOSentiment classifier works by examining the order of Negation, AnA-words, and lexicon words. In this regard, we have the following possible positions:  
* Negation before AnA-words;
* AnA-words before Negation. The lexicon word can be found either at the beginning, end, or in the middle between the word from the AnA-words list and Negation. The BOSentiment classifier provided in the above Figure checks for these positions and calculates the final sentiment score accordingly. The BOSentiment classifier provided in Figure 2 checks for these positions and calculates the final sentiment score accordingly. The sentiment score can be either \(-1\), \(0\), or \(1\), where \(-1\) represents negative sentiment, \(0\) represents neutral sentiment, and \(1\) represents positive sentiment. Additionally, at times when we were uncertain about the impact of negation and AnA-words on the sentiment analysis of a tweet, we employed a simple method (1), where all other words that were not labeled as \(-1\) or \(1\) were labeled with \(0\). If the resulting value was less than \(0\), then the tweet was assigned a sentimental value of \(-1\). Conversely, if the value was greater than \(0\), the tweet received a sentimental value of \(1\). If the value was exactly \(0\), the tweet was deemed to be neutral. This approach was used as a fallback when the more complex analysis involving negation and AnA-words did not produce a clear sentiment value. While not as nuanced as the more sophisticated approach, this simple method still provides a rough estimate of the sentiment of a tweet. The model first checks whether Negation comes before AnA or vice versa. Depending on the order, it then checks the position of the lexicon word to Negation and AnA. If all three elements (Negation, AnA, and lexicon word) are strictly present in a tweet, the classifier then uses the position and relationship between these three elements to determine the final sentiment value. The code follows a set of conditions to determine the sentiment score. For example, if Negation comes before the AnA-words word, the lexicon word is found between Negation and AnA, and the sentiment value of the tweet is negative, the classifier assigns a sentiment score of \(-1\). Similarly, if AnA comes before Negation, the lexicon word is found at the beginning, and the sentiment value is positive, the classifier assigns a sentiment score of \(1\).