## 2 Related Work  
Different studies suggest that sentiment analysis tools may not produce reliable results when applied to SE texts, primarily due to the lack of annotated datasets for training such a tool [8]. SentiStrength-SE, a dictionary-based tool built on top of SentiStrength, was developed to enhance sentiment analysis in textual contents of SE [9]. The tool's overall performance was evaluated using the F1 score, which measures the balance between precision and recall. The results showed that the tool outperformed SentiStrength, achieving an F1 score of 77.48% compared to SentiStrength's 62.02%.  
The experiments conducted in [10] show that the lexicon approach is effective on domain-specific data. These lexicon-based tools utilize three emotional polarities: positivity, negativity, and neutrality. Our goal is to identify and differentiate between DSSMs and DISMs. We believe that a more nuanced approach would be beneficial, including emotions such as joy, love, neutrality, surprise, anger, and fear. By doing so, we hope to gain a deeper understanding of human interactions. In [11], the authors found that sentiments and emotions including joy and love decrease the fixing time of a task, while negative sentiments and emotions, in particular sadness and anger, increase the addressing time. There is a correlation between the level of politeness in a comment of an issue and the duration it takes to resolve that issue. Specifically, tickets with more polite comments tend to be resolved in a shorter amount of time.  
One potential solution to improve sentiment analysis in DSSMs is to use cross-domain knowledge transfer by leveraging a dataset from a similar domain to the target domain [12]. By using transformer-based models such as BERT, RoBERTa, or XLNet; we aim to fine-tune models that are capable of interpreting hidden meanings from the domain-specific vocabulary. This type of approach is in line with other works that have adapted the pre-trained BERT model to investigate sentiment analysis inside specific domains [13,14].  
In their work, the authors of [15] utilized the pretrained BERT model and customized it for the SE domain. They suggest that the performance of SE models improves because the base BERT vocabulary tokens are substituted with tokens from the field they are trained on. Specifically, half of the base words were replaced with domain-specific vocabulary. Other works compared lexicon-based approaches, such as Stanford CoreNLP, SentiStrength, SentiStrength-SE, SentiCR, and Senti4 SD, with transformer models like BERT, RoBERTa, XLNet, and ALBERT, which were trained on six different datasets in SE domain [16]. The findings indicate that transformer-based models show superior performance when compared to all of the lexicon-based tools tested, as measured by the F1 score.  
Another interesting approach is to examine the use of emoticons in DSSM. In this context, a deep neural network that uses cross-domain techniques to analyze emoticons used in SE texts, named SEntiMoji, was proposed [17]. It is built on top of DeepMoji, a pretrained model that detects emotions in noisy labels, for instance, the emoticons found in Twitter comments [18].  
A comprehensive analysis of DSSMs and DISMs remains absent in the existing literature, with limited attention given to the field of SE. However, exploring DSSMs across various areas could uncover valuable insights into semantics, communication patterns, and practical applications beyond SE contexts.