Fig. 1
This figure depicts the step-by-step methodological framework proposed for tweet sentiment analysis. It begins with (a) data collection and extension, followed by (b) data cleaning and preprocessing. Subsequently, (c) sentiment labeling into positive, neutral, and negative categories is performed using the ‘cardiffnlp/twitter-roberta-base-sentiment-latest’ pre-trained transformer, and the dataset is split into training, validation, and test sets. The framework proceeds with (d) model development, (e) model benchmarking, and evaluation against baseline models or state-of-the-art approaches. Finally, the process concludes with XAI interpretation techniques applied to gain insights into the model’s predictions.
