Supporting affordance detection and extraction in online product reviews

Abstract

Affordances have proved very useful in conceptualizing features of products and the action potential (or action possibility) of those features to users: a pen provides writing functionality, and its action potential is the write-ability—an affordance. This simple formulation is powerful, and yet it is quite deceiving because an affordance is very hard to pin down in practice. That is, beyond simple objects like pens, how do we establish and articulate affordances for novel items like a smartphone? In fact, most scholarly works presume an affordance exists and spend little time justifying its existence. Therefore, rather than presuming their existence, it would be worthwhile to dedicate research effort to discovering affordances empirically in a rigorous and theoretically grounded manner. A fairly underexplored source and a potential mine of “naturally” occurring affordances can be the text of online product reviews. Indeed, this thesis proposes a framework to detect and extract affordances from the text of online product reviews. We employed an online tool that aggregates product reviews from Amazon.com. We then used the dataset of product reviews to annotate the potentially occurring associated affordances. Then three analysts used a tool to assign affordances to the extracted text fragments. We then analyzed the identified affordances as well as the inter-rater agreements associated with these affordances. Subsequently, employing pattern recognition algorithms and techniques, we generated a dataset and used it to identify the potential existence of pseudo-grammatical and part-of-speech patterns in text. We then performed frequency count analysis and visual analytics techniques to highlight dominant patterns that stand out. First, the results point to a useful, albeit preliminary, methodology to extract and identify affordances in text data. Second, the results show the potential existence of distinctive pseudo-grammatical and part-of-speech patterns that can occur as a basis for identifying