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首页> 外文期刊>International journal of synthetic emotions >Sentiment Analysis in the Light of LSTM Recurrent Neural Networks
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Sentiment Analysis in the Light of LSTM Recurrent Neural Networks

机译：LSTM递归神经网络的情感分析

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摘要

Long short-term memory (LSTM) is a special type of recurrent neural network (RNN) architecture that was designed over simple RNNs for modeling temporal sequences and their long-range dependencies more accurately. In this article, the authors work with different types of LSTM architectures for sentiment analysis of movie reviews. It has been showed that LSTM RNNs are more effective than deep neural networks and conventional RNNs for sentiment analysis. Here, the authors explore different architectures associated with LSTM models to study their relative performance on sentiment analysis. A simple LSTM is first constructed and its performance is studied. On subsequent stages, the LSTM layer is stacked one upon another which shows an increase in accuracy. Later the LSTM layers were made bidirectional to convey data both forward and backward in the network. The authors hereby show that a layered deep LSTM with bidirectional connections has better performance in terms of accuracy compared to the simpler versions of LSTM used here.
机译：长短期记忆（LSTM）是一种特殊类型的递归神经网络（RNN）体系结构，它是基于简单RNN设计的，用于更准确地对时间序列及其长期依赖性进行建模。在本文中，作者使用不同类型的LSTM架构进行电影评论的情感分析。研究表明，对于情感分析，LSTM RNN比深度神经网络和常规RNN更有效。在这里，作者探索了与LSTM模型相关的不同体系结构，以研究它们在情感分析上的相对表现。首先构造一个简单的LSTM，并研究其性能。在随后的阶段中，LSTM层彼此堆叠，这表明了精度的提高。后来，使LSTM层成为双向的，以在网络中向前和向后传送数据。作者在此表明，与此处使用的更简单版本的LSTM相比，具有双向连接的分层深度LSTM在准确性方面具有更好的性能。

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