Opinion Mining using Econometrics: A Case Study on Reputation Systems

Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 416–423, Prague, Czech Republic, June 2007. c⃝2007 Association for Computational Linguistics Opinion Mining Using Econometrics: A Case Study on Reputation Systems Anindya Ghose Panagiotis G. Ipeirotis Department of Information, Operations, and Management Sciences Leonard N. Stern School of Business, New York University {aghose,panos,arun}@stern.nyu.edu Arun Sundararajan Abstract Deriving the polarity and strength of opinions is an important research topic, attracting sig- nificant attention over the last few years. In this work, to measure the strength and po- larity of an opinion, we consider the eco- nomic context in which the opinion is eval- uated, instead of using human annotators or linguistic resources. We rely on the fact that text in on-line systems influences the behav- ior of humans and this effect can be observed using some easy-to-measure economic vari- ables, such as revenues or product prices. By reversing the logic, we infer the semantic ori- entation and strength of an opinion by tracing the changes in the associated economic vari- able. In effect, we use econometrics to iden- tify the “economic value of text” and assign a “dollar value” to each opinion phrase, measur- ing sentiment effectively and without the need for manual labeling. We argue that by inter- preting opinions using econometrics, we have the first objective, quantifiable, and context- sensitive 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. 1 Introduction A significant number of websites today allow users to post articles where they express opinions about prod- ucts, firms, people, and so on. For example, users on Amazom.com post reviews about products they bought and users on eBay.com post feedback describ- ing their experiences with sellers. The goal of opinion mining systems is to identify such pieces of the text that express opinions (Breck et al., 2007; K¨onig and Brill, 2006) and then measure the polarity and strength of the expressed opinions. While intuitively the task seems straightforward, there are multiple challenges involved. • What makes an opinion positive or negative? Is there an objective measure for this task? • How can we rank opinions according to their strength? Can we define an objective measure for ranking opinions? • How does the context change the polarity and strength of an opinion and how can we take the context into consideration? To evaluate the polarity and strength of opinions, most of the existing approaches rely either on train- ing from human-annotated data (Hatzivassiloglou and McKeown, 1997), or use linguistic resources (Hu and Liu, 2004; Kim and Hovy, 2004) like WordNet, or rely on co-occurrence statistics (Turney, 2002) be- tween words that are unambiguously positive (e.g., “excellent”) and unambiguously negative (e.g., “hor- rible”). Finally, other approaches rely on reviews with numeric ratings from websites (Pang and Lee, 2002; Dave et …

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