对于产品设计需求分析和细分的社交倾听:图形分析方法与用户评论采矿
Xinjun Lai1, Guitao Huang1, Ziyue Zhao1
1School of Electro-Mechanical Engineering, Guangdong University of Technology, Guangzhou, Guangdong, China.
Big data
|September 5, 2023
概括
本研究从社交媒体评论中提取了客户产品设计需求,使用先进的自然语言处理 (NLP) 和深度学习. 它可以识别用户对产品特征的偏好,从而更快地转化为设计规格.
科学领域:
- 计算社会科学 计算社会科学
- 数据挖掘 数据挖掘
- 人工智能的人工智能
背景情况:
- 了解客户产品设计要求对于市场成功至关重要.
- 分析客户反的传统方法可能耗时且缺乏深度.
- 社交媒体平台为产品分析提供丰富的用户生成数据来源.
研究的目的:
- 开发一个新的框架,从社交媒体评论中提取产品设计要求.
- 为了有效地将用户需求转化为可操作的产品设计规范.
- 根据用户偏好对市场和产品模型进行细分.
主要方法:
- 建议对子网络产生进行指数级歧视性雪球采样.
- 使用自然语言处理 (NLP) 和图形SAmple和aggreGatE来定义用户个性.
- 采用深度学习 (双向LSTM与CRF) 进行意见挖掘和创新评论频率-反向组频率指标.
主要成果:
- 与传统方法相比,实现了更合理的用户群集群.
- 成功地确定了对产品规格的主要用户偏好 (喜欢和不喜欢).
- 在各种用户群体中,对产品功能有着相似和不同的偏好.
结论:
- 拟议的框架有效地挖掘社交媒体数据,以了解客户的设计要求.
- 能够快速识别用户需求,促进更快的产品设计代.
- 为产品开发和营销提供宝贵的管理和工程见解.
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