A Two‐Stage NLP‐Driven Framework for Interval‐Valued Carbon Price Prediction Using Sentiment Analysis and Error Correction

Accurate predictions of carbon prices are essential for efficient administration and stable operation of carbon markets. Previous studies have mostly focused on point or interval predictions based on point‐valued data. These approaches insufficiently capture the full extent of market volatility. In contrast, interval‐valued data, containing maximum and minimum values, enable more meaningful interval‐valued predictions and thus provide a more comprehensive assessment of uncertainty. However, as previous research in this direction is limited, this study proposes a two‐stage framework for interval‐valued prediction using interval‐valued data. During the initial prediction stage, natural language processing (NLP) techniques are employed to analyze textual data from social media to assess market sentiment. This unstructured data (UD) is then combined with structured data (SD) and fed into a convolutional neural network‐bidirectional long short‐term memory‐Attention (CNN‐BiLSTM‐Attention) mechanism to generate an initial prediction. During the error correction (EC) stage, deviations between the actual and initial predicted values are calculated. These error sequences are then predicted and incorporated into the initial prediction to refine the final results. Trading simulations indicate that the proposed SD‐UD‐CNN‐BiLSTM‐Attention‐EC model can reduce trading risk and improve trading returns.