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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 K 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. [ABSTRACT FROM AUTHOR]

For wire-feed laser additive manufacturing (WLAM), the build geometrical parameters are one of the indicators of build quality; thus, it is crucial to monitor the geometrical parameters in real-time for quality assurance. However, the current research and development for in situ geometry monitoring are in the early phase due to interweaved correlation of the sensing data and their comprehensive effects on the bead geometry, as well as the high characterization cost to model these effects. This paper focuses on using machine learning techniques to enable in-process geometry monitoring by comprehensively modeling the correlation between the real-time molten pool sensing data and bead geometry properties. A deep learning-based multi-modality convolutional neural network (m-CNN) is trained to take the molten pool image and thermal profile as the input to comprehensively estimate the geometric properties of the build bead. The network is configured by the hyperparameter optimization process and experimentally validated by the real-time molten pool sensing data collected on a wire-feed laser additive manufacturing (AM) system. The effect of using the temperature data from the leading, center, and tailing positions of the molten pool on the prediction performance of the CNN model is studied and analyzed. The CNN model's performance is compared with a support vector regression model for comparison. The developed model represents an in-process monitoring framework for real-time estimation of post-processing bead geometric properties and takes a step towards developing in situ quality control strategy for the metal AM system. [ABSTRACT FROM AUTHOR]