Abstract: This paper describes the system we designed for our participation to SemEval2023 Task 12 Track 6 about Algerian dialect sentiment analysis. We propose a transformer language model approach combined with a lexicon mixing terms and emojis which is used in a post-processing filtering stage. The Algerian sentiment lexicons was extracted manually from tweets. We report on our experiments on Algerian dialect, where we compare the performance of \textbackslash{}marbert to the one of \textbackslash{}arabicbert and \textbackslash{}camelbert on the training and development datasets of Task 12. We also analyse the contribution of our post processing lexical filtering for sentiment analysis. Our system obtained a F1 score equal to 70\textbackslash{}\%, ranking 9\textbackslash{}raise+.5ex\textbackslash{}hbox\{th\} among 30 participants.