This paper explores the importance of text sentiment analysis and
classification in the field of natural language processing, and proposes a new
approach to sentiment analysis and classification based on the bidirectional
gated recurrent units (GRUs) model. The study firstly analyses the word cloud
model of the text with six sentiment labels, and then carries out data
preprocessing, including the steps of removing special symbols, punctuation
marks, numbers, stop words and non-alphabetic parts. Subsequently, the data set
is divided into training set and test set, and through model training and
testing, it is found that the accuracy of the validation set is increased from
85% to 93% with training, which is an increase of 8%; at the same time, the
loss value of the validation set decreases from 0.7 to 0.1 and tends to be
stable, and the model is gradually close to the actual value, which can
effectively classify the text emotions. The confusion matrix shows that the
accuracy of the model on the test set reaches 94.8%, the precision is 95.9%,
the recall is 99.1%, and the F1 score is 97.4%, which proves that the model has
good generalisation ability and classification effect. Overall, the study
demonstrated an effective method for text sentiment analysis and classification
with satisfactory results.

本研究探讨了在自然语言处理领域中文本情感分析和分类的重要性，并提出了一种基于双向门控循环单元（GRUs）模型的新方法。研究首先分析了包含六个情感标签的词云模型的文本，然后进行了数据预处理，包括删除特殊符号、标点符号、数字、停用词和非字母部分。随后，将数据集划分为训练集和测试集，并通过模型训练和测试，发现在训练过程中验证集的准确度从 85% 提高到 93%，增加了 8%；同时，验证集的损失值从 0.7 降低到 0.1，并趋于稳定，模型逐渐接近实际值，能够有效分类文本的情感。混淆矩阵显示模型对测试集的准确度达到 94.8%，精确度为 95.9%，召回率为 99.1%，F1 分数为 97.4%，证明该模型具有良好的泛化能力和分类效果。总之，本研究展示了一种有效的文本情感分析和分类方法，并取得了令人满意的结果。

基于双向门控循环单元（GRUs）模型的文本情感分析与分类

Text Sentiment Analysis and Classification Based on Bidirectional Gated  Recurrent Units (GRUs) Model

Text sentiment analysis, also known as opinion mining, is research on the
calculation of people's views, evaluations, attitude and emotions expressed by
entities. Text sentiment analysis can be divided into text-level sentiment
analysis, sen-tence-level sentiment analysis and aspect-level sentiment
analysis. Aspect-Based Sentiment Analysis (ABSA) is a fine-grained task in the
field of sentiment analysis, which aims to predict the polarity of aspects. The
research of pre-training neural model has significantly improved the
performance of many natural language processing tasks. In recent years, pre
training model (PTM) has been applied in ABSA. Therefore, there has been a
question, which is whether PTMs contain sufficient syntactic information for
ABSA. In this paper, we explored the recent DeBERTa model (Decoding-enhanced
BERT with disentangled attention) to solve Aspect-Based Sentiment Analysis
problem. DeBERTa is a kind of neural language model based on transformer, which
uses self-supervised learning to pre-train on a large number of original text
corpora. Based on the Local Context Focus (LCF) mechanism, by integrating
DeBERTa model, we purpose a multi-task learning model for aspect-based
sentiment analysis. The experiments result on the most commonly used the laptop
and restaurant datasets of SemEval-2014 and the ACL twitter dataset show that
LCF mechanism with DeBERTa has significant improvement.

本文通过整合 DeBERTa 模型和 Local Context Focus 机制提出了一个多任务学习模型，来解决基于方面的情感分析问题，在 SemEval-2014 以及 ACL Twitter 数据集上取得了显著的进步。