情感识别分析是研究社会文本信息的重要方法。它在社会文本分析和研究中具有重要地位。但目前公文文本情感识别效率低下，多采用人工判断方式，主观意识强。本文旨在研究基于神经网络BERT模型的情感公文文本识别与分析方法。它通过深度学习下的BERT-SVM模型算法提取互联网文本信息中包含的情感信息，进而挖掘用户的情感。对文章中的句子进行情感分析，考虑个人、社会乃至国家的影响因素，将该方法置于分析句子或每个词所代表的不同情感的位置。本文首先介绍了文本情感识别的相关技术，如前馈神经网络、卷积神经网络、递归神经网络等。通过使用LSTM-RNN和LSTM-RNN-word2vec模型进行情感训练，实验表明情感分类的平均准确率为95.12%，
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最近邻结果为 90.87%，贝叶斯分类器为 86.84%。相比之下，BERT-SVM 模型提高了文本情感分类的准确性。
Sentiment recognition analysis is an important method for studying social textual information. It has an important position in social text analysis and research. However, at present, the efficiency of official document text sentiment recognition is low, and manual judgment methods are often used, so the subjective consciousness is strong. This article aims to study the sentiment official document text recognition and analysis method based on the neural network BERT model. It extracts the sentiment information contained in the internet text information through the BERT–SVM model algorithm under deep learning and then mines the user's sentiment. Sentiment analysis is carried out on the sentences in the article, considering the influencing factors of individuals, society, and even the country, putting the method in the position of analyzing the different sentiments represented by a sentence or each word. This article first describes the related technologies of text sentiment recognition, such as feedforward neural networks, convolutional neural networks, and recurrent neural networks. By sentiment training using LSTM-RNN and LSTM-RNN-word2vec models, experiments show that the average accuracy of sentiment classification is 95.12%, the
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nearest neighbors result is 90.87% and the Bayesian classifier is 86.84%. By comparison, the BERT–SVM model improves the accuracy of text sentiment classification.
