Text mining research has grown in importance in recent years due to the
tremendous increase in the volume of unstructured textual data. This has
resulted in immense potential as well as obstacles in the sector, which may be
efficiently addressed with adequate analytical and study methods. Deep
Bidirectional Recurrent Neural Networks are used in this study to analyze
sentiment. The method is categorized as sentiment polarity analysis because it
may generate a dataset with sentiment labels. This dataset can be used to train
and evaluate sentiment analysis models capable of extracting impartial
opinions. This paper describes the Sentiment Analysis-Deep Bidirectional
Recurrent Neural Networks (SA-BDRNN) Scheme, which seeks to overcome the
challenges and maximize the potential of text mining in the context of Big
Data. The current study proposes a SA-DBRNN Scheme that attempts to give a
systematic framework for sentiment analysis in the context of student input on
institution choice. The purpose of this study is to compare the effectiveness
of the proposed SA- DBRNN Scheme to existing frameworks to establish a robust
deep neural network that might serve as an adequate classification model in the
field of sentiment analysis.

利用深度双向循环神经网络进行情感分析的 SA-BDRNN 模型旨在充分发挥文本挖掘在大数据背景下的潜力，通过训练和评估情感分析模型，提取客观意见，并与现有框架进行比较以建立一个强大的深度神经网络分类模型。