Forecasting Artificial Intelligence News Sentiment Index: Traditional vs. Image-Based Deep-Learning Models

Rapid AI adoption has intensified public and media attention toward AI-related developments, making news sentiment an increasingly important indicator of expectations and perceptions. This study constructs a global daily AI News Sentiment Index using data from the GDELT Global Knowledge Graph and examines the forecasting properties of the index using statistical, sequential deep learning and image-based forecasting methods. The period of observation of the dataset spans from January 2016 to December 2025, and the sample consists of 3635 daily observations. A sentiment index is constructed from the positive and negative sentiments and is analyzed together with its constituent series. For the empirical framework, ARIMA, ARIMA–GARCH, ETS, LightGBM, LSTM, CNN, TCN, GAN, and convolutional models based on Gramian Angular Summation Field (GASF) and Gramian Angular Difference Field (GADF) transformations are compared using accuracy evaluation on the out-of-sample test set. In this study, we find no evidence that increasing model complexity leads to better forecasting performance for the AI news sentiment series. In most cases, sequential forecasting models perform better than their image-based counterparts, while GASF and GADF transformations do not deliver consistently better forecasting performance for sentiment series of different types and different forecasting horizons. This result indicates that transforming noisy sentiment time series into an image may hide rather than preserve useful information for forecasting purposes. The study contributes to the growing literature on AI news sentiment forecasting by providing a comprehensive comparison of statistical, sequential, and image-based forecasting paradigms and offers practical insights for researchers, policymakers, and practitioners interested in monitoring AI-related expectations and sentiment dynamics.