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Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics

Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics,10.1109/TKDE.2010.188,IEEE Transactions on

Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics   (Citations: 4)
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With the rapid growth of the Internet, the ability of users to create and publish content has created active electronic communities that provide a wealth of product information. However, the high volume of reviews that are typically published for a single product makes harder for individuals as well as manufacturers to locate the best reviews and understand the true underlying quality of a product. In this paper, we reexamine the impact of reviews on economic outcomes like product sales and see how different factors affect social outcomes such as their perceived usefulness. Our approach explores multiple aspects of review text, such as subjectivity levels, various measures of readability and extent of spelling errors to identify important text-based features. In addition, we also examine multiple reviewer-level features such as average usefulness of past reviews and the self-disclosed identity measures of reviewers that are displayed next to a review. Our econometric analysis reveals that the extent of subjectivity, informativeness, readability, and linguistic correctness in reviews matters in influencing sales and perceived usefulness. Reviews that have a mixture of objective, and highly subjective sentences are negatively associated with product sales, compared to reviews that tend to include only subjective or only objective information. However, such reviews are rated more informative (or helpful) by other users. By using Random Forest-based classifiers, we show that we can accurately predict the impact of reviews on sales and their perceived usefulness. We examine the relative importance of the three broad feature categories: "reviewer-related" features, "review subjectivity" features, and "review readability" features, and find that using any of the three feature sets results in a statistically equivalent performance as in the case of using all available features. This paper is the first study that integrates econometric, text mining, and predictive modeling techniques toward a more complete analysis of the information captured by user-generated online reviews in order to estimate their helpfulness and economic impact. Index Terms—Internet commerce, social media, user-generated content, textmining, word-of-mouth, product reviews, economics, sentiment analysis, online communities. Ç
Journal: IEEE Transactions on Knowledge and Data Engineering - TKDE , vol. 23, no. 10, pp. 1498-1512, 2011
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    • ...We examined 2 text-style features: “subjectivity” and “readability” of reviews [10]...

    Beibei Liet al. Towards a theory model for product search

    • ...We examined the \subjectivity" and \readability" of reviews [5] and measured the percentage of reviewers for each hotel who reveal their real name or location information on their prole web pages...

    Beibei Liet al. A demo search engine for products

    • ...Ghose and Ipeirotis [12] use an econometric approach, text mining and technology of model predictive control to discuss the correlation between product sales and characteristics of reviews including average helpfulness of historical reviews, information richness and readability...

    Hui Fanget al. An Empirical Analysis of the Impact of Online Reviews on Product Sales...

    • ...Most previous work [17, 10, 11, 6, 12, 15] attempts to solve the problem of review evaluation by treating each review as a standalone text document, extracting features from the text and learning a function based on these features for predicting review quality...
    • ...Most previous work [17, 10, 11, 6, 12, 15] has typically focused on automatically determining the quality (or helpfulness, or utility) of reviews by using textual features...
    • ...Ghose and Ipeirotis [6] combined econometric models with textual subjectivity analysis and demonstrated evidence that extreme reviews are considered to be most helpful...

    Yue Luet al. Exploiting social context for review quality prediction

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