Forecasting fund-related textual emotion trends on Weibo: A time series study

Introduction This paper reports a time series analysis of day-to-day emotional text related to fund investments on Weibo (Sina Corporation, Beijing, China). Methods The present study employed web-crawler and text mining techniques through Python to obtain data from January 1, 2021 to December 31, 2021. Results Using an auto-regressive integrated moving average model and vector auto-regressive model, the results indicated that fund performance was a significant predictor of fear, anger, and surprise expressions on Weibo. A relationship among emotions within a certain single fund was not found, but textual emotions could be predicted by ARIMA models within emotions. Discussion The findings provide insight for media emotion analysis combining linguistic and temporal dimensions in both the communication and psychology disciplines.