The combinations of these lower-level attributes form higher-level benefits, or “meta-attributes,” for consumers, such as Hardware and Connectivity, which can provide managers with actionable insights.
Sales teams need to understand the higher-level product benefits that drive consumer buying behavior. Product design teams must communicate with engineering and manufacturing to understand the relationships between the product’s technical specifications and its perceived benefits.
Engineering teams need to be able to estimate the trade-offs of technical subcomponents to build the product model that fulfills the more abstract benefits associated with the product’s meta-attributes.
The traditional method of surveys can be time-consuming and may yield inconsistent results across different sampling periods.
To fill this gap, the research team devised a methodological framework based on machine learning and natural language processing to obtain an embedded representation of product attributes.
Specifically, embedded representation describes (represents) textual data such as individual product attributes using the words that surround such textual data (i.e., the contextual information) in consumer reviews.
The representation is quantified using neural networks that enable mathematically measurement of the degrees of similarity between various product attributes based on how they are described by consumers themselves (i.e., the contextual information), thus revealing similarities and differences in the attributes’ usage by consumers.
From this embedded representation, the model then identifies multi-level clusters of product attributes that reflect the levels of abstract product benefits.
This then enables grouping the product attributes together based on their contextual similarities to uncover higher-level benefits that can influence consumer satisfaction or dissatisfaction with a product.
The sentiments associated with these meta-attributes are used to evaluate objects of managerial interest, such as a product or brand, and then can go deeper to examine which engineered attributes primarily drive consumer sentiments concerning the meta-attributes.
The research makes three main contributions. First, it provides a methodological framework for managers to extract and monitor information related to products and their attributes from consumer reviews.
Second, the research extends sentiment analysis of consumer reviews by demonstrating hierarchical sentiment analysis, which aggregates sentiment scores associated with individual attributes based on an attribute hierarchy.
Third, the study uses consumer reviews of tablets to provide a practical demonstration of the method. In particular, it analyzes consumer sentiments about Hewlett-Packard and Toshiba to explore potential reasons why these brands ultimately discontinued their tablet product lines.