Twitter’s exceptional growth as a stage where the public speaks has made it almost a compulsory platform for data sources for sentiment analysis. The site contains informal languages, abbreviations, and problematical phrases which complicate traditional ways of sentiment classification. An attempt is made to analyze deep learning models for the computation of sentiment analysis on Twitter. Therefore, this paper compares four SOTA methods: LSTM+RNN, LSTM+CNN, BiLSTM, and BERT, and uses a corpus of 1.6 million labeled tweets. These four models are analyzed from various points, such as contextual understanding, accuracy, and resource demand. As was demonstrated through experiments, BERT shows remarkable accuracy and an F1-score due to the presence of deep bidirectional attention and contextual embeddings. Using a BiLSTM that encoded both past and future dependencies, it also recorded good performance. In this case, hybrid models based on LSTM could also achieve similar scores with a way lower training cost. On the whole, the research underlines that specific deployment constraints consisting of latency and hardware are to be considered in the choice of the model. Developing tools for sentiment analysis with unstructured social media data is a challenge. However, the findings present practical solutions for researchers and practitioners. The results of the study reveal that among all tested models, transformer-based models were the best to utilize for the extraction of complex sentiments, such as those in tweets. BERT was able to achieve the highest of 84% accuracy among all the models, confirming its effectiveness in handling complex Twitter data.