Research on Online Reputation of Goods Based on Emotional Analysis

Yan, Xiaotong; Zhao, Zhijie; Han, Xiaoxu; Fan, Zhipeng; Zhang, Jialin

doi:10.1007/978-3-030-63784-2_21

Cited by 1 publication

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References 15 publications

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“…In this paper, the text sentiment analysis technology is used to deeply mine the text information of online famous brand reviews, to reflect the attributes and attitudes of consumers. Grab the mobile phone review data and search engine index for verification, to show the online word-of-mouth of commercial brands, consumers' attention to various attributes of goods, emotional attitudes, and so on (Yan et al, 2021 ). Local commercial brands are regarded as tools of birthplace image.…”

Section: Introductionmentioning

confidence: 99%

Business Brand Research Based on Multi-Feature Fusion Emotion Analysis

2022

Front. Psychol.

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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 to achieve the purpose of improving the competitiveness of enterprises. The research of commercial brands 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 toward goods or services by analyzing texts. In this paper, the linear support vector machine model is used to extract and study the features in sentiment analysis. Then, the brand based on the 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 the deep learning word vector model. Support Vector Machine (SVM) maps feature vectors to some sample points in the space, finds a plane that can realize sample classification, and maximizes the distance between the two sample points closest to the plane in two types of samples, to maximize the classification performance and improve the generalization of the model.

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Section: Introductionmentioning

confidence: 99%

Business Brand Research Based on Multi-Feature Fusion Emotion Analysis

2022

Front. Psychol.

View full text Add to dashboard Cite

show abstract

Research on Online Reputation of Goods Based on Emotional Analysis

Cited by 1 publication

References 15 publications

Business Brand Research Based on Multi-Feature Fusion Emotion Analysis

Business Brand Research Based on Multi-Feature Fusion Emotion Analysis

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