基于用户生成内容的产品机会信息收集方法数据集

为解决现有产品性能评价方法仅评估单一产品性能而不考虑其他竞争性产品的问题，课题组研发了基于用户生成内容的产品机会信息收集方法，利用在线平台的用户生成内容数据，结合LDA主题建模、情感分析和机会算法，挖掘竞争性产品的改进机会。技术研发过程中，产生了基于用户生成内容的产品机会信息收集方法数据集，作为课题一相关成果的支撑数据。该数据集为京东网站十大手机品牌官方旗舰店的同价位段手机的买家评论数据，共11940条，主要记录了会员、级别、评价星级、评价内容、时间、点赞数、评论数、追评时间、追评内容、商品属性、页面网址、页面标题、采集时间、sku（库存量单位）、好评度、评价关键词、评论类型、该类型评论数、路径、对此条评论的评论数等字段，数据量为20.6 MB。To address the issue that existing product performance evaluation methods only assess the performance of a single product without considering competing products, our research team developed a user-generated content-based product opportunity information collection method. Leveraging user-generated content data from online platforms and combining LDA topic modeling, sentiment analysis, and opportunity algorithms, this method mines improvement opportunities for competing products. During the technical R&D process, a dataset supporting the aforementioned product opportunity information collection method was generated as supporting data for the relevant achievements of Project 1. This dataset includes 11,940 buyer review records of mobile phones in the same price range from official flagship stores of the top 10 mobile phone brands on JD.com. Its main fields cover: member, user level, review star rating, review content, review time, number of likes, number of comments on the review, additional review time, additional review content, product attributes, page URL, page title, collection time, SKU (Stock Keeping Unit), positive review rate, review keywords, review type, number of reviews of this type, access path, and number of comments on this review. The total data volume of this dataset is 20.6 MB.

搜集汇总

背景与挑战

背景概述

该数据集旨在通过分析用户生成内容来挖掘产品改进机会，解决了传统方法忽略竞争性产品的问题。它基于京东手机评论数据，采用LDA主题建模和情感分析等技术，包含11940条记录，涵盖评价内容、时间等多个字段。