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基于LDA的可穿戴設(shè)備在線評論主題挖掘研究

發(fā)布時間:2018-02-21 14:57

  本文關(guān)鍵詞: 智能手環(huán) 可穿戴設(shè)備 LDA 用戶滿意度 在線評論 主題挖掘 出處:《信息資源管理學(xué)報》2017年03期  論文類型:期刊論文


【摘要】:本文利用文本挖掘方法分析了智能手環(huán)用戶評論中的熱點主題,以了解用戶對不同品牌智能手環(huán)關(guān)注點差異以及用戶關(guān)注主題隨時間的變化。首先,利用LDA模型對文本進行主題分類,了解不同品牌用戶關(guān)注主題的排序;然后對不同時段各品牌的評論信息進行分析,了解不同用戶群體對產(chǎn)品功能關(guān)注度的變化;最后分析用戶對不同品牌手環(huán)整體滿意度的差異,并結(jié)合評論文本信息分析用戶使用時長與滿意度之間的關(guān)系。本研究發(fā)現(xiàn)對于所有品牌,用戶均關(guān)注產(chǎn)品功能和外觀,以及在使用中發(fā)現(xiàn)的產(chǎn)品問題和感受,但是不同品牌關(guān)注主題排序各有差異。就用戶滿意度而言,隨著使用時間的增長,用戶對小米手環(huán)的滿意度日漸降低,而Fitbit和華為手環(huán)的用戶滿意度與使用時間呈正U形曲線。
[Abstract]:In this paper, a text mining method is used to analyze the hot topics in user reviews of smart bracelet, in order to understand the difference of user's attention to different brands of smart bracelet and the change of user's concern over time. The LDA model is used to classify the theme of the text to understand the ranking of the topics concerned by the users of different brands, and then to analyze the comments of different brands in different periods to understand the changes of the attention of different user groups to the function of the product. Finally, the paper analyzes the difference of users' overall satisfaction with different brands of bracelet, and analyzes the relationship between the length of time used by users and their satisfaction with the commentary text information. This study finds that for all brands, the users are concerned about the function and appearance of the products. And the problems and feelings found in the use of the products, but different brands pay attention to the theme of different ranking. In terms of user satisfaction, as the use of time, the user's satisfaction with the Xiaomi bracelet gradually decreased, Fitbit and Huawei bracelet user satisfaction and use time positive U-shaped curve.
【作者單位】: 武漢大學(xué)信息管理學(xué)院;武漢大學(xué)電子商務(wù)研究與發(fā)展中心;
【基金】:國家自然科學(xué)基金項目“內(nèi)容關(guān)系互動下的在線醫(yī)療社區(qū)用戶行為演化研究”(71573197)和“物聯(lián)網(wǎng)環(huán)境下組織體系架構(gòu)建模、行為分析與優(yōu)化設(shè)計“(71531009)的研究成果之一
【分類號】:G252;TP368.33
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本文編號:1522172

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