Opinion Mining using Econometrics: A Case Study on Reputation Systems

Deriving the polarity and strength of opinions is an important research topic, attracting significant attention over the last few years. In this work, to measure the strength and polarity of an opinion, we consider the economic context in which the opinion is evaluated, instead of using human annotators or linguistic resources. We rely on the fact that text in on-line systems influences the behavior of humans and this effect can be observed using some easy-to-measure economic variables, such as revenues or product prices. By reversing the logic, we infer the semantic orientation and strength of an opinion by tracing the changes in the associated economic variable. In effect, we use econometrics to identify the “economic value of text” and assign a “dollar value” to each opinion phrase, measuring sentiment effectively and without the need for manual labeling. We argue that by interpreting opinions using econometrics, we have the first objective, quantifiable, and contextsensitive evaluation of opinions. We make the discussion concrete by presenting results on the reputation system of Amazon.com. We show that user feedback affects the pricing power of merchants and by measuring their pricing power we can infer the polarity and strength of the underlying feedback postings.