预测和可视化在线社交媒体中的消费者情绪

Liqiao Zhang, Hui Yuan, Raymond Y. K. Lau
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引用次数: 5

摘要

随着社交网络的兴起,用户对在线社交媒体平台上的产品、服务和事件的意见呈爆炸式增长。这些固执己见的评论为企业提供了前所未有的机会,可以利用消费者的集体智慧来加强营销、产品设计和其他重要的业务功能。我们研究工作的主要贡献是在Apache Spark之上设计了一个新颖的社交媒体分析框架,用于根据消费者与其他消费者的关系预测和可视化消费者的意见取向,这些消费者的意见取向是已知的。特别是,我们探索了最先进的集体分类(CC)算法来预测消费者的意见取向。基于从新浪微博收集的真实数据,我们的实验表明,提出的基于Gibbs采样的CC算法既考虑用户的局部特征,也考虑用户的关系特征,优于仅考虑关系特征的另一种方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting and Visualizing Consumer Sentiments in Online Social Media
With the rise of the Social Web, there is an explosive growth of user-contributed opinions toward products, services, and events on online social media platforms. These opinionated comments provide firms with unprecedented opportunities to tap into the collective consumer intelligence for enhancing marketing, product design, and other vital business functions. The main contribution of our research work is the design of a novel social media analytics framework on top of Apache Spark for predicting and visualizing consumers' opinion orientations based on their relationships with other consumers whose opinion orientations are known. In particular, we explore state-of-the-art collective classification (CC) algorithms for predicting consumers' opinion orientations. Based on real-world data collected from Sina Weibo, our experiments show that the proposed Gibbs sampling based CC algorithms which consider both a user's local features and their relational features outperform another approach that considers relational features alone.
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