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Web 2.0 has allowed online shoppers to become not just information seekers but also information providers. Many e-commerce venues allow users to share their experiences in the form of textual product reviews. While textual product reviews represent a wealth of information for candidate buyers, finding pertinent information becomes difficult, as the number of reviews for a particular product becomes large, or if the buyer is interested in particular features of an item.
We present ChatterCrop, a tool that uses text summarization. ChatterCrop condenses the information from a large set of reviews into a few sentences, and allows users to customize these summaries for feature-focused search. In a user study, subjects used ChatterCrop and a sortable list of reviews, to answer questions about two camcorder models. When looking for information about particular features, ChatterCrop outperformed the list, in terms user confidence and perceived ease in finding pertinent information. ChatterCrop’s also provided users with starting points to guide further research, and were particularly helpful in the early stages of sensemaking.
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