Social Media Sentiment Analysis to Predict Market Trends in the Creative Industry
Article Sidebar
-
Creative Industry, Consumer Behavior, Market Trends
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.
Full text article
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
Copyright (c) 2025 Purwati Purwati, Agung Yuliyanto Nugroho, Hamka Hamka, Hendro Sukoco
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
