Symbolic semantic design of industrial products based on Big data technology

Exploring the symbolic semantic design path of industrial products is to make industrial products more compatible with the diverse emotional needs of consumers. In this paper, starting from the sentiment analysis model, the PLSA-FSVM sentiment analysis method is constructed using a probabilistic latent potential semantic analysis method and support vector machine based on the Fisher kernel. The method’s validity is verified for comparative experiments and sentiment word frequency analysis evaluation. From the comparison experiments, the ten-fold cross-average precision and recall of PLSA-FSVM were 89.18% and 88.35%, respectively, 4.15% and 2.59% higher than PLSA-SVM. From the sentiment word frequency analysis, the percentages of sentiment words such as atmosphere, practical, and worthy are 23.08%, 22.59%, and 24.72%, respectively. This shows that the PLSA-FSVM sentiment analysis method can effectively realize the sentiment analysis of industrial product evaluation, promote the symbolic semantic design to be more in line with consumers’ emotional needs, and then realize the symbolic design of industrial products to reach the meaning with shape and enjoy with meaning.