Sentiment analysis, a key component of natural language processing and data mining, plays a pivotal role in extracting subjective insights from textual data, particularly on social media platforms. In response to the growing importance of digital engagement, understanding public sentiment has become essential for formulating effective marketing strategies. This study aims to enhance the marketing strategy of energy products in subsidiaries of State-Owned Enterprises (SOEs) by employing a hybrid sentiment analysis model that integrates the Valence Aware Dictionary and Sentiment Reasoner (VADER) with Long Short-Term Memory (LSTM) neural networks. Utilizing a mixed-method approach that combines both quantitative and qualitative analyses, the study collects and processes data from multiple social media sources to identify and classify consumer sentiment. The results demonstrate that the hybrid VADER-LSTM model achieves an accuracy rate of up to 84%, enabling a more nuanced interpretation of consumer opinions. These insights inform the development of data-driven, responsive, and targeted marketing strategies. Furthermore, the study highlights the significance of fostering interactive communication between companies and consumers to enhance the impact of digital marketing efforts. Theoretical implications include a contribution to the academic discourse on information systems and digital marketing, while practical outcomes offer valuable guidance for SOEs in adopting adaptive, sentiment-informed marketing approaches within the energy sector.