实现了一种基于双向LSTM（BiLSTM）的影评情感分析算法。BiLSTM是双向长短时记忆网络，针对文本、视频等时序数据分析可有效提取上下文依赖关系。影评分析是文本处理的一个子领域，是典型的时序数据处理问题。BiLSTM采用两个反向的LSTM网络，为模型提供了额外的上下文信息。为防止过拟合，在网络中还采用了Dropout机制。本算法基于Keras平台实现，并且通过实验对比，LSTM、BiLSTM和BiLSTM结合Dropout三种算法中，最后一种效果最好。本算法设计为BiLSTM的广阔应用前景提供了研究基础。

This paper implements a film review sentiment analysis algorithm based on BiLSTM. BiLSTM is a bidirectional long-short term memory network. It can effectively extract context dependencies by analyzing time series data such as text and video.Film review analysis is a sub field of text processing, which is a typical time series data processing problem. The BiLSTM uses tworeverse LSTM networks to provide additional context information for the model. In order to prevent over fitting, dropout mechanismis also adopted in the network. This algorithm is implemented based on Keras platform. Through experimental comparison, the lastone is the best among the three algorithms: LSTM, BiLSTM and BiLSTM combined with dropout. The algorithm design provides aresearch basis for the broad application prospect of BiLSTM.