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All Journal PROCEEDING IC-ITECHS 2014
Tang, Na
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Computer Science & IT Education

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Research on the Application of NLP-Driven Opinion Sentiment Tendency Analysis in Precision Marketing of New Energy Vehicles Tang, Na; Daijie, Li; mulang, Bu; Zhongyuan, Liu; Wenjing, Zhu; Chuanxiang, Yang
IC-ITECHS Vol 5 No 1 (2024): IC-ITECHS
Publisher : LPPM STIKI Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/ic-itechs.v5i1.1635

Abstract

In today's online new media era, online public opinion often influences consumers' purchasing decisions. This dissertation discusses the strategies to promote the development of domestic new energy passenger cars, and combines the comment data of major media platforms, so that enterprises can more accurately understand the public's attitudes and perceptions towards new energy vehicles, and formulate more effective marketing strategies to increase the sales of new energy vehicles. This paper utilizes the nlp sentiment analysis technique, and incorporates 15 original papers to collect nearly 20,000 comments from major websites. It is found that the sales of new energy vehicles continue to rise despite a high proportion of negative online opinions about new energy vehicles, which is closely related to scientific and technological innovations, policy promotion, and consumers' increased acceptance of the concept of green mobility.
New Energy Vehicle Brand Sales Trend Forecast and User Evaluation Research Tang, Na; Zeng, Xuefei; Jiang, Ying; Zheng, Haoyu; Zhou, Yiyi; Guo, Yan Lan
IC-ITECHS Vol 5 No 1 (2024): IC-ITECHS
Publisher : LPPM STIKI Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/ic-itechs.v5i1.1637

Abstract

In the context of green economic development, new energy vehicles, as one of the representatives of innovation-driven development and environmental protection and energy conservation, are currently in a leading position in the market. This paper selects eight representative well-known brands, namely BYD, Li Auto, Xiaopeng, Geely, NIO, Changan Automobile, SAIC-GM and ORA, for quantitative analysis, with the aim of identifying future sales trends and conducting a precise business evaluation of new energy vehicles, as well as exploring the key factors affecting sales. The study was empirically tested using ARIMA time series forecasting, principal component analysis, linear regression analysis and neural network models. The results showed that brand identification behavior and personalized products have the most significant positive impact on sales. Therefore, this study believes that car brand collection, forwarding, personalized production and quality control are the key factors and improvement priorities that will affect future sales.
The Innovative Path of AI Technology Empowering Marketing Strategy in Unmanned Supermarkets Tang, Na; Li, Yiding; Liang, Yuting; He, Qiqi; Jiang, Wenxin
IC-ITECHS Vol 5 No 1 (2024): IC-ITECHS
Publisher : LPPM STIKI Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/ic-itechs.v5i1.1638

Abstract

This research , whose research objects are users’ experiences, is a quantitative research t through methods such as literature method, questionnaire survey, and data analysis. The results show that the impact of unmanned supermarket technology on shopping experience, the evaluation of unmanned supermarket service and safety, and the correlation between technical difficulties and shopping frequency are the most important factors affecting user experience. Among them, the diversity of checkout technology and service response speed contribute the most. These analysis results provide important theoretical support and reference for unmanned supermarkets to optimize user experience in the future. At the same time, they are of great significance for optimizing the operation mode of new retail and meeting users' diverse consumption needs. Furthermore, they offer practical insights for enhancing the integration of AI technology to improve service efficiency, operational processes, and customer satisfaction, further supporting the sustainable growth and competitiveness of unmanned supermarkets in the evolving retail landscape.
Depth Study of User Purchase Influencing Factors in Platform E-commerce under the Background of Big Data and AI Tang, Na; Han, Yixuan; Wu, Yanlin; Sun, Fuli; Lai, Xinyue; Huang, Bingyan
IC-ITECHS Vol 5 No 1 (2024): IC-ITECHS
Publisher : LPPM STIKI Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/ic-itechs.v5i1.1639

Abstract

Taking Taobao as a typical platform e-commerce, this study is devoted to exploring how big data and artificial intelligence technology can empower platform e-commerce and affect users' purchase intention. This paper adopts the empirical research method, collects data through online questionnaire, uses spss26 for regression analysis, and adopts the gradient lifting algorithm of machine learning model for data verification. The research results show that marketing activities such as "precise placement", "personal privacy", "product details" and "product ranking", which rely on big data and artificial intelligence technology, are the key factors affecting Taobao users' purchase intention, and their impact coefficients are 0.57, 0.135 and 0.288 respectively. Inventory management, profile building and personalized recommendations are also important factors. This paper takes the Consumer Behavior Analysis Model (AISAS) as the theoretical basis, and puts forward corresponding suggestions for Taobao platform e-commerce to enhance user attention, interest, search, purchase and sharing under the background of big data and artificial intelligence technology application.
Co-Authors Chuanxiang, Yang Daijie, Li Guo, Yan Lan Han, Yixuan He, Qiqi Huang, Bingyan Jiang, Wenxin Jiang, Ying Lai, Xinyue Li, Yiding Liang, Yuting mulang, Bu Sun, Fuli Wenjing, Zhu Wu, Yanlin Zeng, Xuefei Zheng, Haoyu Zhongyuan, Liu Zhou, Yiyi