historical information-event emotion dataset

This dataset, constructed around the Jilin Baishan Incident, aims to enhance the emotion prediction capabilities of large language models. Approximately 3.5 million raw comments were collected via the Weibo API, covering key information such as user identifiers, text content, timestamps, and interaction metrics. The data underwent preprocessing steps including normalization, Chinese tokenization, stopword removal, deduplication, and anomalous sample exclusion.The innovation lies in the batch processing approach that maximizes the value of users' historical data, segmenting each user's historical Weibo records chronologically into training samples of 50 entries per batch, assigned with consistent emotion labels reflecting the user's stance toward the target event. This method effectively overcomes the context length constraints of large language models while substantially expanding the training dataset size and enhancing the model's capacity to capture temporal patterns.All training samples exclude users' direct comments on the target event, ensuring the model performs genuine prediction tasks rather than simple recognition tasks. This dataset is particularly suitable for studying emotional evolution and prediction in social events, providing rich resources for sentiment analysis and social computing research.