Work

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

Year: 2016

Type: conference-paper

Abstract: 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.The proposed CLSTM outperforms the state-of-the-art models on three publicly available document-level sentiment analysis datasets.

Source: Conference on Empirical Methods in Natural Language Processing (EMNLP)

DOI: 10.18653/v1/d16-1172

Authors: Jiacheng Xu, Danlu Chen, Xipeng Qiu, Xuanjing Huang

Institution: Fudan University

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: National Natural Science Foundation of China

Awards: G3892115987, 基于深度神经网络的端到端自动问答系统研究, G7433387973