1. Market Value of Online Product Reviews--- A Sentiment Mining Approach (Julian) Chenhui Guo The University of Arizona, Tucson 85721, AZ chguo@email.arizona.edu chguo@email.arizona.edu

2. Research background Electronic markets such as eBay and Amazon sell millions of products to the customers every day. How can customers make decisions to buy? Star-scoring and Number of reviews are useful more importantly, textual reviews

3. An Example of textual reviews

4. Sentiment Mining generates textural review scores We define the sentiment measures of a piece of review r on a certain product A at time t as a vector S. The weights of each sentiment measure can be defined as vector W.

5. Aggregation of review scores So, the overall product review score is the dot product of vector S and vector W. A vendor’s product website can have multiple customers’ reviews about a certain product. Suppose there are R product reviews that are available to the customers. We aggregate the Score of R reviews of product A at time t as follows.

6. Multinomial Logit Model we adopt Multinomial Logit (MNL) Market-Share Model. The basic assumption of this model is that customers statistically tend to choose the products with highest value. By controlling the fixed value of a certain products, the author can test the alternative effects on the market share of the product at a time. Our estimation equation based on MNL is as follows.

7. Use Market Share as the Dependent Variable Here, the dependent variable is Logarithm of Market Share.

8. Rewrite the equation Since some variables vary only depending on time, we can rewrite the equation as follows. Now, let’s pay attention to the most important terms---. Since is a fixed variable when t is fixed, we only need to consider about. The IV is effect of product review. (1)

9. Control of Information cascade, WOM, and Stars Since information cascades can also influence market share, we need to control it. Previous literature shows information cascades can be controlled by adding Rank of Popularity of the products into the model (Duan et al., 2009). Words-Of-Mouth effect can help increase the reputation of a certain product, because the increased “buzz” brings more attention to the product (Pang & Lee, 2008). Previous study showed that number of reviews may simply reflect the “words of mouth” effect. Many online buying websites (e.g. Amazon) contain 5-star-scoring of a certain product. So, it is necessary to control the recommendation effect of star- scoring. (2) (3) (4)

10. The final regression model (Q & A) So, (5) is our estimation equation. After putting (1), (2), (3), and (4) into (5) and rewriting the model, we get the final estimation equation (6). WOM can interact with product review effect, so we put the interaction term. (5) (6)