Research on Credit Evaluation Model of Online Store Based on SnowNLP

Abstract

The online store credit rating is a reflection of the seller's integrity and the quality of the product. The level of the credit rating directly affects the buyer's desire to purchase. Two important factors affecting the credit rating are data and models. The innovation of this research is that the collected data comes from the second evaluation, and the credit evaluation model is improved based on the snowNLP tool, and the malicious brushing filtering function is added. Compared with the credit evaluation system commonly used in current online stores, the evaluation results of the paper are more accurate, detailed and intuitive, and may effectively reduce false brushing and threat review.

How Do Multiple Actors Conduct Science Communication About Omicron on Weibo: A Mixed-Method Study

“…For comment sentiment recognition, we used SnowNLP, a dictionary-based Python database for Chinese sentiment analysis (Chen et al, 2018). Sentiment analysis with big data usually includes sentiment dictionaries and machine learning.…”

“…Sentiment analysis with big data usually includes sentiment dictionaries and machine learning. A sentiment dictionary is suitable for low-granularity texts (with shorter lengths), with the advantage of speedy procedures and high accuracy (Chen et al, 2018). We employed the Snow NLP (sample words shown in Table 3) for sentiment analysis because the sample comments were mostly short sentences or texts (Lan, 2013).…”

Explore the relationship between textual sentiments and pictures' emotions based on unit quantification

“…Some presented models [2,3,10,15] quantify textual sentiments by comparing word separation and vector similarity. Although the model is not specifically trained to recognize sentiment, it incorporates significant image domain knowledge.…”

“…Eq. (2) shows the calculation methods to solve the two major questions mentioned in this paper. Here T stands for textual sentiments, and P stands for pictures' emotions.…”

“…Under the help of such predictable stock movements, stock trading software can hopefully achieve better performance in both automation and accuracy. With the emergence of programmable libraries such as jieba [2] and SnowNLP [3], more and more scholars began to study quantitative finance [4][5][6][7] and set out to establish mappings from textual content to stock indices [8] in terms of textual syllogisms, textual word vectors, and textual similarity [9][10]. However, usually, every regular news article contains not only texts but also pictures.…”