Dynamic Evolution Mechanism of Digital Entrepreneurship Ecosystem Based on Text Sentiment Computing Analysis - Li, Jiahui; Yao, Meifang (Frontiers in Psychology, 2021)
In order to solve the limitations of the current entrepreneurial ecosystem, the research on the digital entrepreneurial ecosystem is more meaningful. This paper aims to study the dynamic evolution mechanism of digital entrepreneurship ecosystem based on text sentiment computing analysis. This paper proposes an improved Bi-LSTM model, which uses a multi-layer neural network to deal with classification problems. It has higher accuracy, recall and F1 value than the traditional LSTM model, and can better perform sentiment analysis on text. The algorithm uses the optimized Naive Bayes algorithm, which is based on Euclidean distance weighting, and can assign different weights to the final classification results according to different attributes. Compared with the general Bayes algorithm, it improves the calculation efficiency and can better match the digital entrepreneurial ecosystem is evolving dynamically, predicting and analyzing its future development. The experimental results in this paper show that the improved Bi-LSTM is better than the traditional Bi-LSTM model in terms of accuracy and F1 value. The accuracy rate is increased by 1.1%, the F1 value is increased by 0.6%, and the recall rate is only less than 0.2%. Running on the spark platform, although 3% accuracy is sacrificed, the running time is increased by 320%. The performance improvement brought by the huge data set is very huge, which fully proves the feasibility of the digital entrepreneurship ecosystem Sex.