Twitter-based classification for integrated source data of weather observations

Abstract

Meteorology and weather forecasting are crucial for predicting future climate conditions. Forecasts can be helpful when they provide information that can assist people in making better decisions. People today use big data to analyze social media information accurately, including those who rely on the weather forecast. Recent years have seen the widespread use of machine learning and deep learning for managing messages on social media sites like Twitter. In this study, authors analyzed weather-related text in Indonesia based on the searches made on Twitter. A total of three machine learning algorithms were examined: support vector machine (SVM), multinomial logistic regression (MLR), and multinomial Naive Bayes (MNB), as well as the pretrained bidirectional encoder representations of transformers (BERT), which was fine-tuned over multiple layers to ensure effective classification. The accuracy of the BERT model, calculated using the F1-score of 99%, was higher than that of any other machine learning method. Those results have been incorporated into a web-based weather information system. The classification result was mapped using Esri Maps application programming interface (API) based on the geolocation of the data.