Vertaile menetelmiä

	Aspect-Based Review Mining	ZMET (Zaltman Metaphor Elicitation Technique)
Tieteenala	Markkinointi	Markkinointi
Menetelmäperhe	Machine learning	Process / pipeline
Syntyvuosi	2004	1995
Kehittäjä	Minqing Hu & Bing Liu	Gerald Zaltman (with Robin Higie Coulter)
Tyyppi	NLP pipeline for feature-level opinion mining of consumer reviews	Image-based depth-interview pipeline for eliciting deep metaphors
Alkuperäislähde	Hu, M., & Liu, B. (2004). Mining and Summarizing Customer Reviews. Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '04), 168-177.	Zaltman, G. (2003). How Customers Think: Essential Insights into the Mind of the Market. Boston, MA: Harvard Business School Press. ISBN: 9781578518265
Rinnakkaisnimet	Feature-Based Opinion Mining, Product-Feature Sentiment Analysis, Review Opinion Mining, Feature-Level Sentiment Summarization	Zaltman Metaphor Elicitation Technique, Metaphor Elicitation, Deep Metaphor Research, Image-Based Consumer Interviewing
Liittyvät	3	3
Tiivistelmä	Aspect-based review mining is a natural-language-processing technique that turns large volumes of consumer reviews into feature-level opinion summaries useful for product and brand insight. Rather than scoring a review as merely positive or negative overall, it identifies the specific product features, or aspects, that customers comment on, the battery life, screen, price, customer service, and so on, and determines the sentiment expressed toward each. Minqing Hu and Bing Liu's 2004 KDD paper, Mining and Summarizing Customer Reviews, defined the canonical pipeline: extract the frequently mentioned features, find the opinion words associated with them, decide each opinion's polarity, and produce a feature-by-feature summary of how many reviewers praised or criticized each aspect. This granularity is what makes the method valuable to marketers, because a four-star product can hide a beloved design and a hated battery, and only feature-level analysis reveals it. Applied across a brand's reviews, it yields a structured map of product strengths and weaknesses straight from the voice of the customer. It scales qualitative listening to thousands or millions of reviews that no team could read by hand.	The Zaltman Metaphor Elicitation Technique (ZMET) is a qualitative consumer-research method that uses images and metaphor to surface the deep, often non-conscious thoughts and feelings that drive how people relate to a brand, product, or experience. Developed by Gerald Zaltman and applied with Robin Higie Coulter, it rests on the premises that most communication is non-verbal, that thought is image-based and metaphorical, and that much of what shapes behavior lies below conscious awareness. Participants gather their own pictures representing their feelings about a topic before a lengthy depth interview, in which a trained interviewer probes the stories behind the images to move from surface metaphors to a small set of universal deep metaphors such as balance, transformation, connection, and journey. Across participants, the elicited constructs and their connections are combined into a consensus map of the shared mental model. Zaltman's 2003 book How Customers Think and the 1995 Journal of Advertising Research article with Coulter set out the technique and its rationale. ZMET aims to hear the voice of the customer in the visual, metaphorical terms in which people actually think.