Sicheng Wan1, Jiajin Tang2 and Xinyue Yang3
1 College of plant protection, Southwest University, Chongqing, 400715
2 Hanhong college, Southwest University, Chongqing, 400100
3 School of Geosciences, Southwest University, Chongqing, 400715
With the rapid development of the Internet and smart phones, in recent years, major global e-commerce service industries have attracted a large number of online sellers. At the same time, most consumers have also transformed traditional mall shopping into a convenient and fashionable online shopping model. When consumers purchase products on the e-commerce platform, most consumers will be able to make purchases and make certain evaluations and star ratings based on their preferences. This not only helps other consumers to have a preliminary understanding of the product before making a purchase, and to make judgments. At the same time, online sellers and e-commerce platforms can conduct background big data analysis and processing based on the indicators evaluated by users, and make a future Forecast the sales volume of goods in the market over time, and upgrade and improve the goods.
text sentiment extraction, principal component analysis, regression analysis
Sicheng Wan, Jiajin Tang and Xinyue Yang. Research on Big Data of Customer Product Reviews Based on Text Sentiment Extraction and Statistical Analysis. Academic Journal of Humanities & Social Sciences (2020) Vol. 3, Issue 3: 21-25. https://doi.org/10.25236/AJHSS.2020.030303.
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