## 6 Conclusions  
Our research used semi-structured information collected from social media posts and various forms of the Naive Bayes classifier to examine the identification of political orientation of Twitter users, based on the content of their posts, as Democrat and Republican classification. The second objective was to calculate performance metrics related to several machine learning models and algorithms in order to make a comparison of their accuracy on the proposed data set.  
The models described and used in this paper, based on the Naive Bayes algorithm suite, generated classification results highly similar to the threshold imposed by the current state of the art, with accuracy around 85% for identifying the political orientation of users based on their tweets. The performances of political orientation identification from tweet posts were analyzed according to the metrics used in the industry, and the results were satisfactory. The models are valid and can be used in specialized NLP applications to classify political views from social media posts.  
According to the performances measured, for the data set proposed in this paper, the Multinomial Naive Bayes algorithm represented the best choice from the set of proposed models. Moreover, the importance of choosing and implementing a model with a distribution compatible with the data set and the scope of the work, or using available calibration methods, is pinpointed. Thus, the nature of the data set used is an important factor in the further development of application based on the concepts presented in this work. In addition, through the work conducted, we have confirmed that the representatives of a political party use terms representative to the party ideology in their posts on social networks.  
Thus, in this paper, it is demonstrated that the use of Twitter posts represents a good starting point in the study of the behavior and language used by individuals in the social media area. As practical applicability, the application can be used in real case scenarios to predict the election results, to monitor and evaluate the evolution of political preferences of social media users, to establish if a politician or political party grows or decreases in popularity and even to predict the dynamics of political movements. Moreover, as future developments and applications, the research results can be used for some practical contexts, such as:  
* Studying users' political orientation and evolution in time, as a result of following and the interactions with politicians;
* Analyzing the fake news promoted by politicians and how the public is influenced;
* Detecting bots specialized in spreading (fake) political views.  
Conceptualization, A.O., A.C., and S.-A.G.; methodology, A.O., A.C.; implementation, S.-A.G.; validation, A.O., A.C., and S.-A.G.; formal analysis, A.O., A.C., and S.-A.G.; writing--original draft preparation, A.O., A.C., and S.-A.G.; writing--review and editing, A.O. and A.C.; supervision, A.O. and A.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.