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.

翻译：摘要：近年来，随着非结构化文本数据量的急剧增长，文本挖掘研究的重要性日益凸显。这一领域既蕴含着巨大潜力，也面临诸多挑战，而通过充分的分析与研究手段可有效应对这些难题。本研究采用深度双向循环神经网络进行情感分析。该方法可生成带有情感标签的数据集，因此被归类为情感极性分析。该数据集可用于训练和评估能够提取客观观点的情感分析模型。本文介绍了情感分析-深度双向循环神经网络（SA-BDRNN）方案，旨在克服文本挖掘在大数据背景下的挑战并最大化其潜力。本研究提出的SA-DBRNN方案试图为院校选择情境下的学生反馈情感分析提供系统化框架。本研究的目的是将所提出的SA-DBRNN方案与现有框架进行有效性对比，以建立稳健的深度神经网络，使其能作为情感分析领域中的有效分类模型。