Forecasting Shallot Prices in Indonesia Using News-Based Sentiment Indicators

Atikah Salsabila; Rani Nooraeni

doi:10.15575/join.v10i1.1422

Authors

Atikah Salsabila Department of Statistical Computing, Politeknik Statistika STIS, Jakarta, Indonesia
Rani Nooraeni Department of Statistical Computing, Politeknik Statistika STIS, Jakarta, Indonesia

DOI:

https://doi.org/10.15575/join.v10i1.1422

Keywords:

Online news, Price Forecasting, Sentiment analysis, Text mining, Time Series

Abstract

The volatile price changes of shallots are a challenge in controlling their prices. The fluctuation in the price of shallots is always reported in the media because it affects people's lives. The news is released online via the internet and has beneficial information so it can be utilized. This study aims to provide a comparative analysis of forecasting models for shallot prices in Indonesia, evaluating the impact of using the most effective sentiment indicators derived from four lexicon-based methods. Data were collected by scraping method on three news portals and one food price information source website during the period from 2020 to 2023. The correlation and causality analysis was conducted to determine the relationship between food prices and sentiment indicators that was obtained using four sentiment analysis methods. The selected sentiment indicators for each day were used as an additional variable in forecasting using ARIMA, SARIMA, and BSTS models. The results showed that the use of news sentiment could reduce RMSE, MAPE, and MAE in forecasting shallot food prices.

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