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Growing Science » International Journal of Data and Network Science » Sentiment analysis on social media using VADER and LSTM to optimise the marketing strategy for SOE energy products

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International Journal of Data and Network Science

ISSN 2561-8156 (Online) - ISSN 2561-8148 (Print)
Quarterly Publication
Volume 10 Issue 1 pp. 87-96 , 2026

Sentiment analysis on social media using VADER and LSTM to optimise the marketing strategy for SOE energy products Pages 87-96 Right click to download the paper Download PDF

Authors: Cornelius Damar Sasongko, R. Rizal Isnanto, Aris Puji Widodo

DOI: 10.5267/j.ijdns.2025.10.011

Keywords: Energy Products, LSTM, Marketing Strategy, Sentiment Analysis, Social Media Marketing, State-Owned Enterprises, VADER

Abstract: 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.

How to cite this paper
Sasongko, C., Isnanto, R & Widodo, A. (2026). Sentiment analysis on social media using VADER and LSTM to optimise the marketing strategy for SOE energy products.International Journal of Data and Network Science, 10(1), 87-96.

Refrences
Akimova, O. E., Volkov, S. K., & Efimov, E. G. (2023). Reasons for sedentarism of shrinking cities in public opinion (evidence from Volgograd city). Population and Economics, 7(1), 77–89. https://doi.org/10.3897/POPECON.7.E87602 Assimakopoulos, C., Antoniadis, I., Kayas, O. G., & Dvizac, D. (2017). Effective social media marketing strategy: Facebook as an opportunity for universities. International Journal of Retail & Distribution Management, 45(5), 532-549. Ashley, C., & Tuten, T. (2015). Creative strategies in social media marketing: An exploratory study of branded social content and consumer engagement. Psychology & marketing, 32(1), 15-27. Bagastio, K., Oetama, R. S., & Ramadhan, A. (2023). Development of stock price prediction system using Flask framework and LSTM algorithm. Journal of Infrastructure, Policy and Development, 7(3). https://doi.org/10.24294/jipd.v7i3.2631 Balaji, P., & Haritha, D. (2023). An Ensemble Multi-layered Sentiment Analysis Model (EMLSA) for Classifying the Complex Datasets. International Journal of Advanced Computer Science and Applications, 14(3), 185–190. https://doi.org/10.14569/IJACSA.2023.0140320 Berestova, A., Kim, D. Y., & Kim, S. Y. (2022). Consumers’ Active Reaction to Brands Taking Stands on Public Issues on Twitter. Sustainability (Switzerland), 14(1). https://doi.org/10.3390/su14010567 Bharathi, R., Bhavani, R., & Priya, R. (2023). Leveraging Deep Learning Models for Automated Aspect Based Sentiment Analysis and Classification. SSRG International Journal of Electrical and Electronics Engineering, 10(5), 120–130. https://doi.org/10.14445/23488379/IJEEE-V10I5P111 Chiny, M., Chihab, M., Chihab, Y., & Bencharef, O. (2021). LSTM, VADER and TF-IDF based Hybrid Sentiment Analysis Model. International Journal of Advanced Computer Science and Applications, 12(7), 265–275. https://doi.org/10.14569/IJACSA.2021.0120730 Ciekanowski, Z., & Wyrębek, H. (2020). Impact of micro, small and medium-sized enterprises on economic security. Polish Journal of Management Studies, 22(1), 86–102. https://doi.org/10.17512/pjms.2020.22.1.06 Dash, G., Sharma, C., & Sharma, S. (2023). Sustainable Marketing and the Role of Social Media: An Experimental Study Using Natural Language Processing (NLP). Sustainability (Switzerland), 15(6). https://doi.org/10.3390/su15065443 Dubbelink, S. I., Herrando, C., & Constantinides, E. (2021). Social media marketing as a branding strategy in extraordinary times: Lessons from the COVID-19 pandemic. Sustainability, 13(18), 10310. Ezzine, H. (2024). Corporate e-Governance as a Determinant of Digital Transformation. Recherches En Sciences de Gestion, 159(6), 327–347. https://doi.org/10.3917/resg.159.0327 Haddaoui, B. El, Chiheb, R., Faizi, R., & Afia, A. El. (2022). LSTM based models stability in the context of Sentiment Analysis for social media. 2021(MoroccoAI), 4–6. Hou, J. (2024). How Does Corporate Social Responsibility Affect Corporate Productivity? The Role of Environmental Regulation. Sustainability (Switzerland), 16(15). https://doi.org/10.3390/su16156426 Fahmi, K., Sihotang, M., Hadinegoro, R. H., Sulastri, E., Cahyono, Y., & Megah, S. I. (2022). Health Care SMEs Products Marketing Strategy: How the Role of Digital Marketing Technology through Social Media?. UJoST- Universal Journal of Science and Technology, 1(1), 16–22. https://doi.org/10.11111/ujost.v1i1.55 Jacobson, J., Gruzd, A., & Hernández-García, Á. (2020). Social media marketing: Who is watching the watchers? Journal of Retailing and Consumer Services, 53, 101774. https://doi.org/10.1016/j.jretconser.2019.03.001 Kaur, G., & Sharma, A. (2023). A deep learning-based model using hybrid feature extraction approach for consumer sentiment analysis. Journal of Big Data, 10(1). https://doi.org/10.1186/s40537-022-00680-6 Kondraganti, A., Narayanamurthy, G., & Sharifi, H. (2022). A systematic literature review on the use of big data analytics in humanitarian and disaster operations. Annals of Operations Research, 335(3), 1015–1052. https://doi.org/10.1007/s10479-022-04904-z Koupidis, K., Bratsas, C., & Vlachokostas, C. (2022). OpΕnergy: An Intelligent System for Monitoring EU Energy Strategy Using EU Open Data. Energies, 15(21). https://doi.org/10.3390/en15218294 Kumar, S. A., Nasralla, M. M., García-Magariño, I., & Kumar, H. (2021). A machine-learning scraping tool for data fusion in the analysis of sentiments about pandemics for supporting business decisions with human-centric AI explanations. PeerJ Computer Science, 7, 1–18. https://doi.org/10.7717/PEERJ-CS.713 Kurniawan, A. (2022). Digital Marketing-Based Tourism Planning Policy in Order to Realize Regional Tourism Competitiveness. Journal of Industrial Engineering & Management Research, 3(6), 185-190. Khiong, K. (2022). Impact and Challenges of Digital Marketing in Healthcare Industries during Digital Era and Covid-19 Pandemic. Journal of Industrial Engineering & Management Research, 3(5), 112-118. Lakon, C. M., Zheng, Y., & Pechmann, C. (2024). Social network tie functions of social support and social influence and adult smoking abstinence. PLoS ONE, 19(3 March). https://doi.org/10.1371/journal.pone.0296458 Li, R., Li, R., & Lin, S. (2022). The embedding path of design innovation in garment enterprises’ high-quality development. Journal of Silk, 59(10), 80–88. https://doi.org/10.3969/j.issn.1001-7003.2022.09.011 Li, F., Larimo, J., & Leonidou, L. C. (2021). Social media marketing strategy: definition, conceptualization, taxonomy, validation, and future agenda. Journal of the Academy of Marketing Science, 49, 51-70. Liu, Y. (2023). Analysis of music teaching technology based on a data mining model. Applied Mathematics and Nonlinear Sciences, 8(2), 3013–3022. https://doi.org/10.2478/amns.2023.2.00017 Mahrukh, R., & Malik, A. S. (2023). Sentiments analysis of fMRI using automatically generated stimuli labels under naturalistic paradigm. Dental Science Reports, 13(1). https://doi.org/10.1038/s41598-023-33734-7 Malesev, S., & Cherry, M. (2021). Digital and social media marketing-growing market share for construction smes. Construction Economics and Building, 21(1), 65–82. https://doi.org/10.5130/AJCEB.v21i1.7521 Mujahid, M., Lee, E., Rustam, F., Washington, P. B., Ullah, S., Reshi, A. A., & Ashraf, I. (2021). Sentiment analysis and topic modeling on tweets about online education during covid-19. Applied Sciences (Switzerland), 11(18). https://doi.org/10.3390/app11188438 Movsisyan, S. A. (2016). Social media marketing strategy of Yerevan brandy company. Annals of Agrarian Science, 14(3), 243-248. Nobre, H., & Silva, D. (2014). Social network marketing strategy and SME strategy benefits. Journal of Transnational Management, 19(2), 138-151. Öztamur, D., & Karakadılar, İ. S. (2014). Exploring the role of social media for SMEs: as a new marketing strategy tool for the firm performance perspective. Procedia-Social and behavioral sciences, 150, 511-520. Parveen, N., Chakrabarti, P., Hung, B. T., & Shaik, A. (2023). Twitter sentiment analysis using hybrid gated attention recurrent network. Journal of Big Data, 10(1), 1–29. https://doi.org/10.1186/s40537-023-00726-3 Praditya, R. A., & Purwanto, A. (2024). The Role of Viral Marketing, Brand Image and Brand Awareness on Purchasing Decisions. PROFESOR: Professional Education Studies and Operations Research, 1(01), 11-15. Prayuda, R. Z. (2024). Investigating the Role of Digital marketing, price perception, customer satisfaction and its impact on marketing performance. Journal of Industrial Engineering & Management Research, 5(2), 25-30. Ray, A., Bala, P. K., & Rana, N. P. (2021). Exploring the drivers of customers’ brand attitudes of online travel agency services: A text-mining based approach. Journal of Business Research, 128, 391–404. https://doi.org/10.1016/J.JBUSRES.2021.02.028 Rosário, A. T., & Dias, J. C. (2023). Marketing strategies on social media platforms. International Journal of E-Business Research (IJEBR), 19(1), 1-25. Sabbeh, S. F., & Fasihuddin, H. A. (2023). A Comparative Analysis of Word Embedding and Deep Learning for Arabic Sentiment Classification. Electronics (Switzerland), 12(6). https://doi.org/10.3390/electronics12061425 Saravanakumar, M., & SuganthaLakshmi, T. (2012). Social media marketing. Life science journal, 9(4), 4444-4451. SHEN, Y., LYU, M., ZHU, J., & XU, G. (2024). Corporate philanthropy, managers optimism and overinvestment: Based on top managers’ psychological biases. Journal of Industrial Engineering and Engineering Management, 38(2), 77–89. https://doi.org/10.13587/j.cnki.jieem.2024.02.006 Sheuly, S. S., Barua, S., Begum, S., Ahmed, M. U., Güclü, E., & Osbakk, M. (2021). Data analytics using statistical methods and machine learning: a case study of power transfer units. International Journal of Advanced Manufacturing Technology, 114(5–6), 1859–1870. https://doi.org/10.1007/s00170-021-06979-7 Shonubi, O. A. (2024). Advancing organisational technology readiness and convergence of emerging digital technologies (AI, IoT, I4.0) for innovation adoption. International Journal of Technology and Globalisation, 9(1), 50–91. https://doi.org/10.1504/IJTG.2024.142621 Shubita, M. F. (2023). Relationship between marketing strategy and profitability in industrial firms: Evidence from Jordan. Innovative Marketing, 19(2), 17–26. https://doi.org/10.21511/im.19(2).2023.02 Silva, M., Walker, J., Portillo, E., & Dougherty, L. (2022). Strengthening the Merci Mon Héros Campaign Through Adaptive Management: Application of Social Listening Methodology. JMIR Public Health and Surveillance, 8(6), 1–9. https://doi.org/10.2196/35663 Sianipar, A., Angelia, W., & Abdau, A. (2025). Digital Dynamic Marketing Capabilities in Green Hospitality: A Systematic Literature Review in the Emerging Countries Context. INTERNATIONAL JOURNAL OF SOCIAL, POLICY AND LAW, 6(3), 75-95. Sun, S., & Long, J. (2024). Marketing Strategy of Private Enterprises Based on Bayesian Dynamic Panel Model of Machine Learning Algorithms. International Journal of Information Technology and Web Engineering, 19(1). https://doi.org/10.4018/IJITWE.344834 Wang, W., Liang, Q., Mahto, R. V, Deng, W., & Zhang, S. X. (2020). Entrepreneurial entry: The role of social media. Technological Forecasting and Social Change, 161. https://doi.org/10.1016/j.techfore.2020.120337 Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. In Artificial Intelligence Review (Vol. 55, Issue 7). Springer Netherlands. https://doi.org/10.1007/s10462-022-10144-1 Xu, H., Liu, Y., Song, B., Yin, X., & Li, X. (2024). Local social network structure and promotion effectiveness in social commerce. Information Technology and People, 37(2), 700–728. https://doi.org/10.1108/ITP-10-2021-0737 Yan, R., Li, X., & Zhu, X. (2022). The Impact of Corporate Social Responsibility on Sustainable Innovation: A Case in China’s Heavy Pollution Industry. Frontiers in Psychology, 13(July). https://doi.org/10.3389/fpsyg.2022.946570 Yin, H., Song, X., Yang, S., & Li, J. (2022). Sentiment analysis and topic modeling for COVID-19 vaccine discussions. World Wide Web, 25(3), 1067–1083. https://doi.org/10.1007/s11280-022-01029-y Yılmaz, M. K., & Altunay, H. T. (2023). Marketing insight from consumer reviews: Creating brand position through opinion mining approach. Telematics and Informatics Reports, 11(August). https://doi.org/10.1016/j.teler.2023.100094 Zgarni, A., & Gharbi, L. (2021). Strategic capabilities and competitive strategies: The moderating role of exporting. International Journal of Business Innovation and Research, 26(1), 58–81. https://doi.org/10.1504/IJBIR.2021.117749 Zhang, X., Quah, C. H., & Nazri Bin Mohd Nor, M. (2023). Deep neural network-based analysis of the impact of ambidextrous innovation and social networks on firm performance. Scientific Reports, 13(1), 1–10. https://doi.org/10.1038/s41598-023-36920-9
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Journal: International Journal of Data and Network Science | Year: 2026 | Volume: 10 | Issue: 1 | Views: 1027 | Reviews: 0

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