Analysis of decision-making strategies in social networks

In the era of social media, platforms have become integral to various domains, particularly business, where trends significantly influence decision-making processes. Despite numerous studies, effective decision-making in social networks remains a challenge. This study addresses these issues by proposing a novel model for analyzing decision-making strategies. A publicly available dataset containing social media user reviews of various products, including attributes such as identification, labels, country, and sentiment, is utilized. The dataset undergoes preprocessing and normalization, incorporating techniques such as tokenization, lemmatization, stop-word removal, and punctuation elimination. Deep learning methodologies are applied for model development and analysis, leveraging Python and the PyCharm framework. The proposed model is rigorously validated using state-of-the-art techniques and evaluated through extensive testing to measure its performance in terms of accuracy. Comparative analysis with recent methods underscores the effectiveness of the model. The findings of this study offer valuable insights for improving decision-making strategies, guiding new product development, and integrating diverse analytical models in social network contexts.