论文标题
情感分析使用简化的长期记忆复发性神经网络
Sentiment Analysis Using Simplified Long Short-term Memory Recurrent Neural Networks
论文作者
论文摘要
LSTM或长期记忆网络是一种特定类型的复发性神经网络（RNN），在处理长序列数据和学习长期依赖性方面非常有效。在这项工作中，我们对共和党辩论Twitter数据集进行了情感分析。为了加快训练并降低计算成本和时间，提出了六个不同的参数减少LSTM模型（Slim LSTM）。我们在数据集上评估了其中两个模型。比较了这两个LSTM模型以及标准LSTM模型的性能。还研究了双向LSTM层的影响。这项工作还包括一项研究，除了为不同的LSTM模型建立最佳的超级参数外，还可以选择最佳体系结构。
LSTM or Long Short Term Memory Networks is a specific type of Recurrent Neural Network (RNN) that is very effective in dealing with long sequence data and learning long term dependencies. In this work, we perform sentiment analysis on a GOP Debate Twitter dataset. To speed up training and reduce the computational cost and time, six different parameter reduced slim versions of the LSTM model (slim LSTM) are proposed. We evaluate two of these models on the dataset. The performance of these two LSTM models along with the standard LSTM model is compared. The effect of Bidirectional LSTM Layers is also studied. The work also consists of a study to choose the best architecture, apart from establishing the best set of hyper parameters for different LSTM Models.
