Social Media Sentiment Analysis to Predict Market Trends in the Creative Industry

Purwati Purwati (1), Agung Yuliyanto Nugroho (2), Hamka Hamka (3), Hendro Sukoco (4)
(1) Politeknik Negeri Sriwijaya, Indonesia,
(2) Universitas Cendekia Mitra Indonesia, Indonesia,
(3) Universitas Lambung Mangkurat, Indonesia,
(4) Universitas Nahdlatul Ulama Purwokerto, Indonesia

Abstract

The rise of social media has transformed how information spreads, creating an invaluable resource for analyzing market trends. In the creative industry, where consumer preferences shift rapidly, understanding social media sentiment is critical for businesses aiming to stay ahead of trends. Previous research on sentiment analysis has shown its potential in various fields, but its specific application in the creative industry remains underexplored. This research aims to investigate how social media sentiment analysis can predict market trends in the creative industry. By analyzing social media posts, reviews, and discussions, the study seeks to explore how positive, negative, and neutral sentiments influence market behavior and creative products’ success. The study employs a combination of data mining and sentiment analysis techniques to analyze social media content related to key creative products. Using machine learning algorithms, the research categorizes posts into sentiment categories and correlates them with market trends, such as sales and consumer behavior. A dataset consisting of social media content from multiple platforms over the past year was utilized for analysis. The results show that positive social media sentiment correlates with increased consumer engagement and sales in the creative industry, while negative sentiment predicts a decline in product success.

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References

Al-Natour, S., & Turetken, O. (2020). A comparative assessment of sentiment analysis and star ratings for consumer reviews. International Journal of Information Management, 54, 102132. https://doi.org/10.1016/j.ijinfomgt.2020.102132

Alsaeedi, A., & Zubair, M. (2019). A Study on Sentiment Analysis Techniques of Twitter Data. International Journal of Advanced Computer Science and Applications, 10(2). https://doi.org/10.14569/IJACSA.2019.0100248

Ansari, M. Z., Aziz, M. B., Siddiqui, M. O., Mehra, H., & Singh, K. P. (2020). Analysis of Political Sentiment Orientations on Twitter. Procedia Computer Science, 167, 1821–1828. https://doi.org/10.1016/j.procs.2020.03.201

Araque, O., Zhu, G., & Iglesias, C. A. (2019). A semantic similarity-based perspective of affect lexicons for sentiment analysis. Knowledge-Based Systems, 165, 346–359. https://doi.org/10.1016/j.knosys.2018.12.005

Araújo, M., Pereira, A., & Benevenuto, F. (2020). A comparative study of machine translation for multilingual sentence-level sentiment analysis. Information Sciences, 512, 1078–1102. https://doi.org/10.1016/j.ins.2019.10.031

Audrezet, A., De Kerviler, G., & Guidry Moulard, J. (2020). Authenticity under threat: When social media influencers need to go beyond self-presentation. Journal of Business Research, 117, 557–569. https://doi.org/10.1016/j.jbusres.2018.07.008

Ben Hassen, T., El Bilali, H., & Al-Maadeed, M. (2020). Agri-Food Markets in Qatar: Drivers, Trends, and Policy Responses. Sustainability, 12(9), 3643. https://doi.org/10.3390/su12093643

Bruns, A. (2019). After the ‘APIcalypse’: Social media platforms and their fight against critical scholarly research. Information, Communication & Society, 22(11), 1544–1566. https://doi.org/10.1080/1369118X.2019.1637447

Burdisso, S. G., Errecalde, M., & Montes-y-Gómez, M. (2019). A text classification framework for simple and effective early depression detection over social media streams. Expert Systems with Applications, 133, 182–197. https://doi.org/10.1016/j.eswa.2019.05.023

Dashtipour, K., Gogate, M., Li, J., Jiang, F., Kong, B., & Hussain, A. (2020). A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks. Neurocomputing, 380, 1–10. https://doi.org/10.1016/j.neucom.2019.10.009

Dolan, R., Seo, Y., & Kemper, J. (2019). Complaining practices on social media in tourism: A value co-creation and co-destruction perspective. Tourism Management, 73, 35–45. https://doi.org/10.1016/j.tourman.2019.01.017

Egbuna, C., Sawicka, B., Tijjani, H., Kryeziu, T. L., Ifemeje, J. C., Skiba, D., & Lukong, C. B. (2020). Biopesticides, Safety Issues and Market Trends. In Natural Remedies for Pest, Disease and Weed Control (pp. 43–53). Elsevier. https://doi.org/10.1016/B978-0-12-819304-4.00004-X

Fiorilli, C., Buonomo, I., Romano, L., Passiatore, Y., Iezzi, D. F., Santoro, P. E., Benevene, P., & Pepe, A. (2020). Teacher Confidence in Professional Training: The Predictive Roles of Engagement and Burnout. Sustainability, 12(16), 6345. https://doi.org/10.3390/su12166345

Ghanem, B., Rosso, P., & Rangel, F. (2020). An Emotional Analysis of False Information in Social Media and News Articles. ACM Transactions on Internet Technology, 20(2), 1–18. https://doi.org/10.1145/3381750

Higdon, R. D., & Chapman, K. (2020). A dramatic existence: Undergraduate preparations for a creative life in the performance industries. Industry and Higher Education, 34(4), 272–283. https://doi.org/10.1177/0950422220912979

Iankova, S., Davies, I., Archer-Brown, C., Marder, B., & Yau, A. (2019). A comparison of social media marketing between B2B, B2C and mixed business models. Industrial Marketing Management, 81, 169–179. https://doi.org/10.1016/j.indmarman.2018.01.001

Ivie, E. J., Pettitt, A., Moses, L. J., & Allen, N. B. (2020). A meta-analysis of the association between adolescent social media use and depressive symptoms. Journal of Affective Disorders, 275, 165–174. https://doi.org/10.1016/j.jad.2020.06.014

Joseph, P., Searing, A., Watson, C., & McKeague, J. (2020). Alternative Proteins: Market Research on Consumer Trends and Emerging Landscape. Meat and Muscle Biology, 4(2). https://doi.org/10.22175/mmb.11225

Keles, B., McCrae, N., & Grealish, A. (2020). A systematic review: The influence of social media on depression, anxiety and psychological distress in adolescents. International Journal of Adolescence and Youth, 25(1), 79–93. https://doi.org/10.1080/02673843.2019.1590851

Leong, L.-Y., Hew, T.-S., Ooi, K.-B., Lee, V.-H., & Hew, J.-J. (2019). A hybrid SEM-neural network analysis of social media addiction. Expert Systems with Applications, 133, 296–316. https://doi.org/10.1016/j.eswa.2019.05.024

Long, J., Chen, Z., He, W., Wu, T., & Ren, J. (2020). An integrated framework of deep learning and knowledge graph for prediction of stock price trend: An application in Chinese stock exchange market. Applied Soft Computing, 91, 106205. https://doi.org/10.1016/j.asoc.2020.106205

Maguluri, L. P., & Ragupathy, R. (2020). A Cluster based Non-Linear Regression Framework for Periodic Multi-Stock Trend Prediction on Real Time Stock Market Data. International Journal of Advanced Computer Science and Applications, 11(9). https://doi.org/10.14569/IJACSA.2020.0110965

Morente-Molinera, J. A., Kou, G., Pang, C., Cabrerizo, F. J., & Herrera-Viedma, E. (2019). An automatic procedure to create fuzzy ontologies from users’ opinions using sentiment analysis procedures and multi-granular fuzzy linguistic modelling methods. Information Sciences, 476, 222–238. https://doi.org/10.1016/j.ins.2018.10.022

Mowlaei, M. E., Saniee Abadeh, M., & Keshavarz, H. (2020). Aspect-based sentiment analysis using adaptive aspect-based lexicons. Expert Systems with Applications, 148, 113234. https://doi.org/10.1016/j.eswa.2020.113234

Nemlioglu, I. (2019). A Comparative Analysis of Intellectual Property Rights: A case of Developed versus Developing Countries. Procedia Computer Science, 158, 988–998. https://doi.org/10.1016/j.procs.2019.09.140

Oueslati, O., Cambria, E., HajHmida, M. B., & Ounelli, H. (2020). A review of sentiment analysis research in Arabic language. Future Generation Computer Systems, 112, 408–430. https://doi.org/10.1016/j.future.2020.05.034

Oussous, A., Benjelloun, F.-Z., Lahcen, A. A., & Belfkih, S. (2020). ASA: A framework for Arabic sentiment analysis. Journal of Information Science, 46(4), 544–559. https://doi.org/10.1177/0165551519849516

Potvin Kent, M., Pauzé, E., Roy, E., De Billy, N., & Czoli, C. (2019). Children and adolescents’ exposure to food and beverage marketing in social media apps. Pediatric Obesity, 14(6), e12508. https://doi.org/10.1111/ijpo.12508

Saiphoo, A. N., & Vahedi, Z. (2019). A meta-analytic review of the relationship between social media use and body image disturbance. Computers in Human Behavior, 101, 259–275. https://doi.org/10.1016/j.chb.2019.07.028

Setiadi, B. R. (2020). A Survey of Engineering Student’s in The Creative Industries Sub-Sectors. Journal of Advanced Research in Dynamical and Control Systems, 12(01-Special Issue), 369–372. https://doi.org/10.5373/JARDCS/V12SP1/20201083

Shen, C., Min Chen, & Wang, C. (2019). Analyzing the trend of O2O commerce by bilingual text mining on social media. Computers in Human Behavior, 101, 474–483. https://doi.org/10.1016/j.chb.2018.09.031

Tajvidi, M., Richard, M.-O., Wang, Y., & Hajli, N. (2020). Brand co-creation through social commerce information sharing: The role of social media. Journal of Business Research, 121, 476–486. https://doi.org/10.1016/j.jbusres.2018.06.008

Tang, F., Fu, L., Yao, B., & Xu, W. (2019). Aspect based fine-grained sentiment analysis for online reviews. Information Sciences, 488, 190–204. https://doi.org/10.1016/j.ins.2019.02.064

Tao, J., Ho, C.-Y., Luo, S., & Sheng, Y. (2019). Agglomeration economies in creative industries. Regional Science and Urban Economics, 77, 141–154. https://doi.org/10.1016/j.regsciurbeco.2019.04.002

Thorisdottir, I. E., Sigurvinsdottir, R., Asgeirsdottir, B. B., Allegrante, J. P., & Sigfusdottir, I. D. (2019). Active and Passive Social Media Use and Symptoms of Anxiety and Depressed Mood Among Icelandic Adolescents. Cyberpsychology, Behavior, and Social Networking, 22(8), 535–542. https://doi.org/10.1089/cyber.2019.0079

Voorveld, H. A. M. (2019). Brand Communication in Social Media: A Research Agenda. Journal of Advertising, 48(1), 14–26. https://doi.org/10.1080/00913367.2019.1588808

Wo?k, K. (2020). Advanced social media sentiment analysis for short?term cryptocurrency price prediction. Expert Systems, 37(2), e12493. https://doi.org/10.1111/exsy.12493

Yang, C., Zhang, H., Jiang, B., & Li, K. (2019). Aspect-based sentiment analysis with alternating coattention networks. Information Processing & Management, 56(3), 463–478. https://doi.org/10.1016/j.ipm.2018.12.004

Zhang, Y., Song, D., Li, X., Zhang, P., Wang, P., Rong, L., Yu, G., & Wang, B. (2020). A Quantum-Like multimodal network framework for modeling interaction dynamics in multiparty conversational sentiment analysis. Information Fusion, 62, 14–31. https://doi.org/10.1016/j.inffus.2020.04.003

Zhao, Z., Zhu, H., Xue, Z., Liu, Z., Tian, J., Chua, M. C. H., & Liu, M. (2019). An image-text consistency driven multimodal sentiment analysis approach for social media. Information Processing & Management, 56(6), 102097. https://doi.org/10.1016/j.ipm.2019.102097

Zhu, B., Zheng, X., Liu, H., Li, J., & Wang, P. (2020). Analysis of spatiotemporal characteristics of big data on social media sentiment with COVID-19 epidemic topics. Chaos, Solitons & Fractals, 140, 110123. https://doi.org/10.1016/j.chaos.2020.110123

Authors

Purwati Purwati
purwati65@gmail.com (Primary Contact)
Agung Yuliyanto Nugroho
Hamka Hamka
Hendro Sukoco
Purwati, P., Nugroho, A. Y., Hamka, H., & Sukoco, H. (2025). Social Media Sentiment Analysis to Predict Market Trends in the Creative Industry. Journal of Social Entrepreneurship and Creative Technology, 2(1), 36–46. https://doi.org/10.70177/jseact.v2i1.2052

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