Publication

Abstract : In order to identify and analyse the thoughts, feelings, and sentiments indicated in textual data, the field of sentiment analysis is crucial. Deep learning models have become effective tools for sentiment analysis thanks to their capacity to recognise semantic links and understand complicated patterns. In-depth analysis of the use of deep learning algorithms for sentiment analysis tasks is presented in this research report. We investigate different deep learning architectures, such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformer-based models, to assess how well they are able to capture nuances in sentiment and contextual information. On benchmark datasets, numerous trials are run, and the models are assessed using important performance indicators as accuracy, precision, recall, and F1 score. applications that are in this field. we also investigate methods like pre- trained word embeddings and transfer learning. The study also investigates the effects of regularisation and hyperparameter tuning methods on the accuracy and applicability of models. In addition, we discuss issues with sentiment analysis's class imbalance, model interpretability, and data pre-processing. The findings show that deep learning models regularly perform better than conventional machine learning techniques, attaining cutting-edge performance in sentiment classification tasks. The results of this study open the door for more advanced applications in this field by giving important new insights into the capabilities and constraints of deep learning models for sentiment analysis.