DeeBERT-S3WD: Three-Way Multigranularity Decision for Interactive Information Sentiment Analysis Research

摘要

Interactive information between listed companies and investors is an emerging form of information disclosure in the stock market and has a crucial impact on stock market analysis. Interactive information interferes with investors’ decision-making by influencing their sentiments, significantly affecting the stock market’s health. Due to the unique nature of interactive information, the existing approaches to dynamic interactive information sentiment analysis rarely consider a multistep study under the tradeoff of cost and accuracy. To address this problem, we propose a novel unified framework combining DeeBERT with the sequential three-way decision based on the early exiting mechanism and continually mining uncertain regions for interactive information sentiment analysis. Specifically, we treat the question-answer pair interaction information as an entire sample and leverage DeeBERT to allow the samples to exit early without traversing through all the Transformer layers. Subsequently, the uncertain samples are selected at each Transformer layer to be reinvestigated at the next granularity of time-evolving data. Besides, we utilize a manual modification method to assign the determined samples to training sets to update the model. Lastly, a series of comparative experiments demonstrate that our proposed model has outstanding performance in terms of time efficiency and interactive information sentiment index.