In recent years, the widespread use of social networks has empowered online users to freely share their opinions on diverse aspects of life. Sentiment Analysis (SA) has consequently emerged as a pivotal domain within Natural Language Processing (NLP), serving a crucial role in discerning sentiment orientations and extracting valuable insights from public viewpoints. Analyzing sentiment in Arabic poses distinctive challenges due to its varied dialects, as well as its intricate morphological and syntactic structures. Deep Learning (DL) models, particularly Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), have exhibited remarkable proficiency in Sentiment Analysis. LSTM networks excel in capturing sequential data patterns, while CNNs offer inherent advantages in feature selection, yielding superior performance compared to conventional machine learning (ML) algorithms. In our study, we propose an ensemble approach that integrates CNN and LSTM techniques to classify and forecast sentiment in tweets. We evaluate the effectiveness of this hybrid model against individual LSTM and CNN methodologies employing the FastText word embedding model. Experimental findings illustrate that our LSTM-CNN hybrid approach, leveraging the FastText word embedding model, significantly improves text classification accuracy.