AUTHOR=Li Boxuan 

TITLE=Business Brand Research Based on Multi-Feature Fusion Emotion Analysis

JOURNAL=Frontiers in Psychology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2022.939304

DOI=10.3389/fpsyg.2022.939304

ISSN=1664-1078

ABSTRACT=With the deepening of globalization, brand plays an important role in determining the competitiveness of enterprises. It is worth thinking about how to quantify the brand value reasonably in order to achieve the purpose of improving the competitiveness of enterprises. The research of commercial brand based on emotion analysis extracts the views of consumers on the evaluation data of brand attributes, analyzes the emotional tendency of consumers' views, and then helps enterprises adjust their production strategies. The purpose of emotion analysis is to judge users' views and attitudes towards goods or services by analyzing texts, In this paper, the linear support vector machine model calculation method is used to extract and study the features in emotion analysis, Then, the brand based on fusion model is evaluated, and the experimental conclusions are drawn: the method of fusing the depth features extracted by word vector and the named entity extracted by CRF makes the brand effect of fusing emotional factors better than the feature extraction method only using CRF named entity recognition; It is proved that the model method proposed in this paper has a certain role in actual business. Feature fusion often combines shallow model methods, and fuses various shallow feature screening technologies based on word segmentation results. This paper introduces fusion features, and supplements feature vectors based on the results of deep learning word vector model.