Persian sentiment analysis of an online store independent of pre-processing using convolutional neural network with fastText embeddings

Adoption of a deep learning-based neural network model in the psychological behavior analysis of resident tourism consumption

With the development of society and the continuous progress of science and technology, it has become the mainstream measure to promote the development of the social economy through science and technology. Therefore, to improve the current situation of tourism consumption, improve the consumer sentiment of tourists, and promote the development of the tourism economy, the convolutional neural network (CNN) technology model is used to analyze the tourist's consumer psychology and behavior. Based on this, the user's consumption situation is analyzed, thus providing support for the intelligent improvement of tourism consumption. First, the basic characteristics of tourism consumption mood and behavior are introduced, and the methods to improve the tourism consumption mood and behavior are briefly introduced. Then, the CNN algorithm is employed to identify consumers' travel consumption behaviors and emotions. To improve the recognition effect, the algorithm is combined with skeleton node behavior recognition and video image behavior recognition. Finally, the performance of the designed algorithm is tested. The accuracy of the human behavior recognition (HBR) algorithm is more than 0.88. Compared with the detection effect of the HBR algorithm, the combined algorithm adopted in this work can reduce the image processing time and improve the detection efficiency. The multithread method can effectively reduce the complexity of the model and improve the recognition accuracy. The test results on different data sets show that the proposed algorithm can better adapt to the changes in identification samples and obtain more accurate recognition results compared with similar algorithms. In summary, this study not only provides technical support for the rational analysis of consumer sentiment and consumer behavior but also contributes to the comprehensive development of the tourism market.

Psychological Education Health Assessment Problems Based on Improved Constructive Neural Network

In order to better assess the mental health status, combining online text data and considering the problems of lexicon sparsity and small lexicon size in feature statistics of word frequency of the traditional linguistic inquiry and word count (LIWC) dictionary, and combining the advantages of constructive neural network (CNN) convolutional neural network in contextual semantic extraction, a CNN-based mental health assessment method is proposed and evaluated with the measurement indicators in CLPsych2017. The results showed that the results obtained from the mental health assessment by CNN were superior in all indicators, in which F1 = 0.51 and ACC = 0.69. Meanwhile, ACC evaluated by FastText, CNN, and CNN + Word2Vec were 0.66, 0.67, 0.67, and F1 were 0.37, 0.47, and 0.49, respectively, which indicates the use of CNN in mental health assessment has feasibility.