Analysis of the trend of global power sources based on comment emotion mining

Abstract

In recent years, renewable energy technologies have been developed vigorously, and related supporting policies have been issued. The developmental trend of different energy sources directly affects the future developmental pattern of the energy and power industry. Energy trend research can be quantified through data statistics and model calculations; however, parameter settings and optimization are difficult, and the analysis results sometimes do not reflect objective reality. This paper proposes an energy and power information analysis method based on emotion mining. This method collects energy commentary news and literature reports from many authoritative media around the world and builds a convolutional neural network model and a text analysis model for topic classification and positive/negative emotion evaluation, which helps obtain text evaluation matrixes for all collected texts. Finally, a long-short-term memory model algorithm is employed to predict the future development prospects and market trends for various types of energy based on the analyzed emotions in different time spans. Experimental results indicate that energy trend analysis based on this method is consistent with the real scenario, has good applicability, and can provide a useful reference for the development of energy and power resources and of other industry areas as well.