LLM GPT-3.5 study for sentiment analysis across Utkarsh server, Ohio supercomputer, Google Colab and PC

• Study of sentiment analysis models on Twitter corpus by utilizing the Large Language Model (LLM), gpt-3-5-turbo-16k version of the Generative Pretrained Transformer (GPT 3.5) model. • Various computational tasks performed using Bidirectional long short-term memory (BiLSTM), Convolutional Neural Networks (CNN), Gated Recurrent Unit (GRU) and Recurrent Neural Network (RNN) using different platforms Ohio Supercomputer, Utkarsh Server, Google Colab and a Personal Computer (PC). • Evaluation of different performance metrics such as precision, recall, F1 score with additional resource tracking metrics like memory usage, time complexity, CPU/GPU utilization. • Achieving efficient model training, optimal performance and cost-effectiveness in research and production environments by balancing the computational demands. The major objective of the present research is to inspect sentiment analysis models that have been trained on Twitter corpus by utilising the Large Language Model (LLM) gpt-3-5-turbo-16k version of the Generative Pretrained Transformer (GPT 3.5) model. Such trained models include the Bidirectional long short-term memory neural network (BiLSTM), Convolutional Neural Networks, Gated Recurrent Unit and Recurrent Neural Network which were used to perform computational tasks on the Ohio Supercomputer and Utkarsh Server, in comparison to work conducted on Google Colab and a Personal Computer (PC). This research also looks at the performance as well as the computational aspects of these models in terms of accuracy, recall, F1-score, time/memory complexity and resource requirements (CPU/GPU) throughout the training and testing phases of each model. The training accuracies in this case were concentrated between 49.91% - 99.98% while those of the testing accuracies of the models accounts for about 50.00% - 75.00%. For instance, models including BiLSTM and RNN usually exhibit more time complexity because of the nature of the models (sequential computation), on the contrary, CNNs are less time-consuming and are more effective in terms of storage modifying layered architecture. The use of supercomputers and specialized servers reduces training time, but resource constraints on platforms such as personal computers or Colab cause considerable divergence.