社会化媒体
计算机科学
数据科学
非结构化数据
社交媒体营销
知识管理
营销
万维网
大数据
业务
数据挖掘
作者
Xin Wang,Neil Bendle,Yinjie Pan
标识
DOI:10.1007/s11747-023-01000-x
摘要
Abstract Analyzing unstructured text, e.g., online reviews and social media, has already made a major impact, yet a vast array of publicly available, unstructured non-text data houses latent insight into consumers and markets. This article focuses on three specific types of such data: image, video, and audio. Many researchers see the potential in analyzing these data sources, going beyond text, but remain unsure about how to gain insights. We review prior research, give practical methodological advice, highlight relevant marketing questions, and suggest avenues for future exploration. Critically, we spotlight the machine learning capabilities of major platforms like AWS, GCP, and Azure, and how they are equipped to handle such data. By evaluating the performance of these platforms in tasks relevant to marketing managers, we aim to guide researchers in optimizing their methodological choices. Our study has significant managerial implications by identifying actionable procedures where abundant data beyond text could be utilized.
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