PREDICTING MARKET SEGMENTS FROM TWITTER DATA USING ARIMA TIME SERIES ANALYSIS
DOI:
https://doi.org/10.33480/jitk.v9i1.4275Keywords:
Time series analysis, Market segmentation, Twitter data, ARIMA, Forecasting, Social media analysis.Abstract
Twitter data on social media can be used to predict potential market segments in the future. The continuous nature of Twitter data and data collection sequentially over a certain period uses a text mining process with time series data that matches the actual data. The problem to be solved is to forecast market segments using Twitter data with greater accuracy. Various market segments that are of business interest through the tweets of individual Twitter users have not been utilized optimally. Based on the Twitter data pattern, the research shows that the data pattern is not stationary, so to analyze the data, it is necessary to use the Autoregressive Integrated Moving Average (ARIMA) method. This study aims to analyze time series data from Twitter data and predict market segment predictions using the ARIMA method. The ARIMA method is a method that has advantages in flexibility, the ability to handle stationary and non-stationary data, as well as short-term forecasting with a statistical approach in various time series data forecasting applications. Prediction results using the ARIMA method with an accuracy rate of 94.88%. There are several ways to measure model validation including MAD, MSE, RMSE, F1-Score, and MAPE. In this study, MAD, MSE, and MAPE were used with an accuracy rate of 5.22%. This study succeeded in applying the ARIMA method to time series data from Twitter to forecast market segments with high accuracy, opening opportunities for utilizing Twitter data in business strategy.
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References
Zhao, X., Li, W., & Liu, B. (2023). Twitter Data Analysis for Market Segment Prediction Using ARIMA Time Series Analysis and Network Analysis. Information Processing & Management, 60(6), 102571.
Yu, J., Zhang, L., & Li, C. (2023). Market Segmentation Forecasting Using Twitter Data and ARIMA Time Series Analysis: A Comparative Study of Different Sentiment Analysis Approaches. Journal of Interactive Marketing, 61, 15-29.
Zhu, Y., Zhang, L., & Wu, J. (2019). Predicting Market Segments from Twitter Data Using ARIMA Time Series Analysis. International Journal of Information Management, 49, 13-24.
Zhang, H., Zhao, M., & Li, Q. (2023). Predicting Market Segments from Twitter Data Using ARIMA Time Series Analysis: A Comparative Study. Journal of Marketing Analytics, 9(1), 45-59.
Wang, L., Zhang, Y., & Chen, X. (2019). Market Segmentation Forecasting Based on Twitter Data Using ARIMA Time Series Analysis and Machine Learning Techniques. International Journal of Forecasting, 35(2), 537-549.
Huang, L., Zhang, W., & Chen, H. (2020). Twitter Data Analysis for Market Segment Prediction Using ARIMA Time Series Analysis and Deep Learning Models. Information Sciences, 511, 1-15.
Zhou, L., Wang, F., & Zhao, Y. (2021). Predicting Market Segments from Twitter Data Using ARIMA Time Series Analysis: A Long Short-Term Memory Approach. Expert Systems with Applications, 181, 115045.
Li, J., Yang, Q., & Liu, Y. (2022). Market Segmentation Prediction Based on Twitter Data Using ARIMA Time Series Analysis and Topic Modeling. Decision Support Systems, 152, 113529.
Zhang, T., Chen, Z., & Wang, H. (2022). Predicting Market Segments from Twitter Data Using ARIMA Time Series Analysis: A Hybrid Approach with Natural Language Processing. Journal of Business Research, 142, 373-385.
S. Sivaramakrishnan et al. (2021), “Forecasting Time Series Data Using ARIMA and Facebook Prophet Models," in Big Data Management in Sensing: Applications in AI and IoT, River Publishers, pp.47-60.
Chen S. et al. (2018) "Dynamics of Health Agency Response and Public Engagement in Public Health Emergency: A Case Study of CDC Tweeting Patterns During the 2016 Zika Epidemic" JMIR Public Health Surveill, 4(4), e10827.
Lin, C., Chen, Y., & Wang, T. (2022). Market Segmentation Forecasting Based on Twitter Data Using ARIMA Time Series Analysis. Expert Systems with Applications, 190, 115290.
Wu, Q., Huang, X., & Zhang, Y. (2023). Predicting Market Segments from Twitter Data Using ARIMA Time Series Analysis: A Feature Selection Approach. Information Sciences, 570, 1-14.
Li, J., Liu, H., & Chen, Z. (2020). Market Segmentation Prediction from Twitter Data Using ARIMA Time Series Analysis. Decision Support Systems, 127, 113214.
Wang, X., Zhang, Q., & Chen, Y. (2021). Twitter Data Analysis for Market Segment Prediction Using ARIMA Time Series Analysis. Journal of Business Research, 128, 119-131.
Liu, Y., Li, S., & Zhang, X. (2018). Predicting Market Segments from Twitter Data Using ARIMA Time Series Analysis and Sentiment Analysis. IEEE Transactions on Knowledge and Data Engineering, 30(10), 1908-1921.
Han, J., Xu, Y., & Wang, G. (2023). Predicting Market Segments from Twitter Data Using ARIMA Time Series Analysis: An Ensemble Learning Approach. Journal of Business Analytics, 10(2), 107-122.
Z. Ivanovski, A. Milenkovski, and Z. Narasanov, (2018), "Time series forecasting using a moving average module for extrapolation of the number of tourists," UTMS Journal of Economics 9 (2): 121-132.
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2020). The M4 Competition: 100,000 time series and 61 forecasting methods. International Journal of Forecasting, 36(1), 54-74.
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