An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object.

This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include material on summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.

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Opinion Mining and Sentiment Analysis

The author systematically examines one of the important issues of information — establishing the market price. He introduces the concept of «search» — where a buyer wanting to get a better price, is forced to question sellers. The article deals with various aspects of finding the necessary information.

Economics of Information

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The Theory of Industrial Organization

We present a lexicon-based approach to extracting sentiment from text. The Semantic Orientation CALculator (SO-CAL) uses dictionaries of words annotated with their semantic orientation (polarity and strength), and incorporates intensification and negation. SO-CAL is applied to the polarity classification task, the process of assigning a positive or negative label to a text that captures the text's opinion towards its main subject matter. We show that SO-CAL's performance is consistent across domains and in completely unseen data. Additionally, we describe the process of dictionary creation, and our use of Mechanical Turk to check dictionaries for consistency and reliability.

Lexicon-based methods for sentiment analysis

Online feedback mechanisms harness the bidirectional communication capabilities of the Internet to engineer large-scale, word-of-mouth networks. Best known so far as a technology for building trust and fostering cooperation in online marketplaces, such as eBay, these mechanisms are poised to have a much wider impact on organizations. Their growing popularity has potentially important implications for a wide range of management activities such as brand building, customer acquisition and retention, product development, and quality assurance. This paper surveys our progress in understanding the new possibilities and challenges that these mechanisms represent. It discusses some important dimensions in which Internet-based feedback mechanisms differ from traditional word-of-mouth networks and surveys the most important issues related to their design, evaluation, and use. It provides an overview of relevant work in game theory and economics on the topic of reputation. It discusses how this body of work is being extended and combined with insights from computer science, management science, sociology, and psychology to take into consideration the special properties of online environments. Finally, it identifies opportunities that this new area presents for operations research/management science (OR/MS) research.

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The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms

Internet auctions have recently gained widespread popularity and are one of the most successful forms of electronic commerce. We examine a unique data set of eBay coin auctions to explore the determinants of bidder and seller behavior. We begin by documenting a number of empirical regularities in our data set of eBay auctions. Next, we specify and estimate a structural econometric model of bidding on eBay. Using our parameter estimates from this model, we measure the extent of the winner’s curse and simulate seller revenue under different reserve prices.

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The Winner's Curse, Reserve Prices and Endogenous Entry: Empirical Insights From eBay Auctions.

We investigate the role that non-portfolio fund differentiation and information/search frictions play in creating two salient features of the mutual fund industry: the large number of funds and the sizeable dispersion in fund fees. In a case study, we find that despite the financial homogeneity of S&P 500 index funds, this sector exhibits the fund proliferation and fee dispersion observed in the broader industry. We show how extra-portfolio mechanisms explain these features. These mechanisms also suggest an explanation for the puzzling late-1990s shift in sector assets to more expensive (and often newly entered) funds: an influx of highinformation-cost novice investors.

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Product Differentiation, Search Costs, and Competition in the Mutual Fund Industry: A Case Study of S&P 500 Index Funds

This paper surveys recent studies of internet auctions. Four main areas of research are summarized. First, we survey several studies that document and attempt to explain the frequently observed sniping, or last-second bidding behavior, in these auctions. Second, we summarize several methods proposed to quantify the distortions caused by asymmetric information in these markets, most notably due to the winner's curse. Third, we explore research about the role of reputation mechanisms installed to help combat these distortions. Finally, we discuss what internet auctions have to teach us about auction design.

Economic Insights from Internet Auctions

This paper studies the economics of match formation using a novel data set obtained from a major online dating service. Using detailed information on the users' attributes and interactions, we estimate a model of mate preferences. Based on the estimated preferences, we use the Gale-Shapley algorithm to predict the stable matches among the users of the dating site. Comparing the predicted and observed matching patterns, we find that the Gale-Shapley model explains the matches achieved by the online dating market well, and that the matches are approximately efficient (within the class of stable matches). We then explore whether the estimated mate preferences, in conjunction with the Gale-Shapley algorithm, can explain the matching patterns in offline, real-world marriages. We find that we can predict assortative mating patterns that are similar to those observed in marriages. As the Gale-Shapley algorithm predicts match outcomes in the absence of search costs, we conclude that mate preferences, not search frictions, explain much of the strong degree of sorting along various attributes in marriages.

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Matching and Sorting in Online Dating

We construct a panel of eBay seller histories and examine the importance of eBay’s reputation mechanism. We find that, when a seller first receives negative feedback, his weekly sales rate drops from a positive 7% to a negative 7%; subsequent negative feedback ratings arrive 25% more rapidly than the first one and don’t have nearly as much impact as the first one. We also find that a seller is more likely to exit the lower his reputation is; and that, just before exiting, sellers receive more negative feedback than their lifetime average. We consider a series of theoretical models and measure them against these empirical results. Regardless of which theoretical model best explains the data, an important conclusion of our paper is that eBay’s reputation system gives way to noticeable strategic responses from both buyers and sellers.

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Ali Hortacsu

Papers

The Winner's Curse, Reserve Prices and Endogenous Entry: Empirical Insights From eBay Auctions.

Product Differentiation, Search Costs, and Competition in the Mutual Fund Industry: A Case Study of S&P 500 Index Funds

Economic Insights from Internet Auctions

Matching and Sorting in Online Dating

The dynamics of seller reputation: evidence from ebay*