Cached Long Short-Term Memory Neural Networks for Document-Level Sentiment Classification

Understand

Recently, neural networks have achieved great success on sentiment classification due to their ability to alleviate feature engineering.

• However, one of the remaining challenges is to model long texts in document-level sentiment classification under a recurrent architecture because of the deficiency of the memory unit.
• To address this problem, we present a Cached Long Short-Term Memory neural networks (CLSTM) to capture the overall semantic information in long texts.
• CLSTM introduces a cache mechanism, which divides memory into several groups with different forgetting rates and thus enables the network to keep sentiment information better within a recurrent unit.