Research on value perception of commercial service of Science and Technology Museum based on Roberta model: taking text analysis as an example

Abstract

Sentiment analysis has gradually become an important content of natural language processing (NLP), and plays an increasingly important role in the fields of system recommendation, user emotion information acquisition, and public opinion reference for governments and enterprises. In the period of comprehensive well-off, the leisure consciousness of Chinese residents has been significantly improved, the income growth has brought about the release of leisure consumption potential, and the time guarantee for leisure has been further enhanced. As the urban public cultural space, the modern Science and Technology Museum bears the diversified spatial functions of knowledge production, cultural empowerment and public welfare. An appropriate range of commercial service supply is an important part of the public policy supply of the Science and Technology Museum. It is very important to understand the emotional tendency of the public for the commercial service of the Science and Technology Museum. Roberta adds a dynamic mask mechanism on the basis of the model Bert, taking a larger amount of pre training data and a larger batch size. This paper introduces a multi-channel mask mechanism on the basis of the Roberta model, and increases the mask ratio, so that the model can learn more levels of emotional information, and the effect on text sentiment analysis is better. Therefore, taking Shanghai Science and Technology Museum as an example, the Roberta model is used to extract and interpret the public perception data of the public comment network, and study the value perception and emotional tendency of the public to the commercial services of the Science and Technology Museum, so as to better guide the Science and Technology Museum to improve the service quality level.