<![CDATA[Objective: This research aims to assess the potential of sentiment analysis based on social media data in predicting market trends and consumer behaviour, as well as evaluate the effectiveness of this approach in supporting business decision-making. Method: Leveraging big data generated in real-time by social media users, this research applies machine learning and Natural Language Processing (NLP)-based approaches to process, analyse, and extract insights from consumer opinions. The research methodology includes collecting data from social media platforms using product- or industry-specific keywords, preprocessing text data, and applying sentiment analysis algorithms. Machine learning models, such as Random Forest and SVM (Support Vector Machine), and lexicon-based approaches, such as VADER (Valence Aware Dictionary and sEntiment Reasoner), were used to measure consumer sentiment. Results: The results show that social media sentiment analysis is effective in identifying public opinion patterns and changes in market trends. The model used is able to provide high accuracy in predicting consumer preferences and helps companies understand market needs more deeply. Novelty: This research confirms that sentiment analysis is a relevant solution to support data-driven decision-making in the digital era, with great potential to be applied in various industry sectors.]]>
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