Unsupervised tip-mining from customer reviews

In recent years, large review-hosting platforms have extended their functionality to allow their users to submit tips: short pieces of text that deliver valuable insight on a specific aspect of the reviewed business. These tips are meant to serve as a concise source of information that complements the often overwhelming number of customer reviews. Recent work has tackled the problem of automatically generating tips by mining review text. The motivation for this effort is to obtain tips for businesses or business aspects that have been overlooked by users. Another motivating factor is the quality of the user-submitted tips, which often provide trivial or redundant information. Existing tip-mining methods are limited by a reliance on training data, which is unlikely to be available and is also very costly to create for different domains. In this work, we present TipSelector, a completely unsupervised algorithm that delivers high quality-tips without the need for annotated training data. We verify the efficacy of TipSelector via an evaluation that includes real data from the hospitality industry and comparisons with the state-of-the-art. A secondary contribution of our work is a method for automatically evaluating tip-mining algorithms without humans in the loop. As we demonstrate in our experiments, this method can be used to enable large-scale evaluations and complement the user studies that are typically used for this purpose.