论文标题
部分可观测时空混沌系统的无模型预测
LSTM based models stability in the context of Sentiment Analysis for social media
论文作者
论文摘要
深度学习技术证明了它们对情感分析（SA）相关任务的有效性。复发性神经网络（RNN），尤其是长期短期记忆（LSTM）和双向LSTM，已成为构建准确预测模型的参考。但是，模型的复杂性和配置的超参数数量提出了几个与它们的稳定性有关的问题。在本文中，我们介绍了各种LSTM模型及其关键参数，并在情感分析的背景下执行实验来测试这些模型的稳定性。
Deep learning techniques have proven their effectiveness for Sentiment Analysis (SA) related tasks. Recurrent neural networks (RNN), especially Long Short-Term Memory (LSTM) and Bidirectional LSTM, have become a reference for building accurate predictive models. However, the models complexity and the number of hyperparameters to configure raises several questions related to their stability. In this paper, we present various LSTM models and their key parameters, and we perform experiments to test the stability of these models in the context of Sentiment Analysis.
