現代互聯網的高速發展使得企業經營模式與互聯網的結合越來越緊密,各行各業都把目光投向了電子商務市場,其中也包括休閒食品行業。休閒食品類企業在電商市場上要想獲得更多的市場份額,它必須要關注其消費者滿意度,因此有必要研究哪些因素會影響到電商市場上消費者的滿意度。本研究使用爬蟲技術獲取電商網站上休閒食品類的在線評論,並引入機器學習與自然語言處理方法,挖掘某電商平臺上休閒類食品的消費者滿意度的主要影響因素,探究各因素與消費者滿意度間的直觀聯繫,為休閒類食品企業提供進一步改善其商品和服務的準確方向。
本研究對某電商平臺休閒類食品的線上評論進行實證研究,對獲取數據的評分進行進一步識別,獲得好評,差評和中評三類。首先本研究將希望從評論中獲取的資訊分為服務、物流、包裝、價格、品質、份量、味道七個維度,利用自然語言處理的相關方法獲得各個維度的分數；最後利用隨機森林方法對影響消費者滿意度的這七個維度進行重要程度的排序。

With the rapid development of e-commerce, enterprises in all walks of life are increasingly focusing on the Internet. In order to obtain more market share in the e-commerce market, leisure food enterprises must pay attention to their consumer satisfaction. Therefore, it is necessary to study which factors will affect the consumer satisfaction in the e-commerce market. This study uses crawler technology to obtain online reviews of leisure food on e-commerce websites, and introduces machine learning and natural language processing methods to mine the main influencing factors of consumer satisfaction of leisure food on an e-commerce platform, and explores the intuitive relationship between each factor and consumer satisfaction, so as to provide the accurate direction for leisure food enterprises to further improve their goods and services.
In this study, the online reviews of leisure food on an e-commerce platform were empirically studied, and the scores of the data obtained were further identified to obtain favorable, poor and medium ratings. First of all, this study divides the information that we hope to get from the reviews into seven dimensions: service, logistics, packaging, price, quality, quantity and taste. Then this study use the methods of natural language processing to get scores of each dimension.Finally, use the random forest model to rank the seven dimensions that affect the degree of importance of consumer satisfaction.

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