A short text sentiment analysis method integrating BiGRU and additive attention

Abstract

In the context of the digital economy, e-commerce and social media have generated massive amounts of short Chinese web texts, making the accurate extraction of sentiment information a critical requirement for market analysis and public opinion monitoring. Short texts are characterized by fragmented information expression, strong context dependency, and sparse features, posing unique challenges for sentiment analysis. To address the shortcomings of traditional methods in terms of semantic understanding depth and scenario adaptability, this study proposes a short-text sentiment analysis method (AddAttn-BiGRU) that integrates BiGRU with Additive Attention. This method employs the BiGRU model to extract semantic information from text context and incorporates a custom Additive Attention mechanism, enabling the model to autonomously learn the emotional importance of different words in short texts, assign differentiated weights, and focus on core emotional words. Validation experiments were conducted on the publicly available Weibo Sentiment 100k dataset. The results showed an accuracy of 97.21% and an F1 score of 97.66%, validating the effectiveness of the model.