In this paper, we focus on online reviews and employ artificial intelligence tools, taken from the cognitive computing field, to help understand the relationships between the textual part of the review and the assigned numerical score. We move from the intuitions that (1) a set of textual reviews expressing different sentiments may feature the same score (and vice-versa), and (2) detecting and analyzing the mismatches between the review content and the actual score may benefit both service providers and consumers, by highlighting specific factors of satisfaction (and dissatisfaction) in texts. To prove the intuitions, we adopt sentiment analysis techniques and we concentrate on hotel reviews, to find polarity mismatches therein. In particular, we first train a text classifier with a set of annotated hotel reviews, taken from the Booking website. Then, we analyze a large dataset, with around 160k hotel reviews collected from TripAdvisor, with the aim of detecting a polarity mismatch, indicating if the textual content of the review is in line, or not, with the associated score. Using well-established artificial intelligence techniques and analyzing in depth the reviews featuring a mismatch between the text polarity and the score, we find that—on a scale of five stars—those reviews ranked with middle scores include a mixture of positive and negative aspects. The approach proposed here, beside acting as a polarity detector, provides an effective selection of reviews—on an initial very large dataset—that may allow both consumers and providers to focus directly on the review subset featuring a text score disagreement,which conveniently convey to the user a summary of positive and negative features of the review target.
E-advice technology offers a form of electronic word-of-mouth'', with new potential for gathering valid suggestions that guides the consumer's choice. Since some years, extensive and nationally representative surveys have been carried out, to evaluate the specific aspects of ratings information that affect people attitudes toward e-commerce''. It is the case, e.g., of work in @cite_15 , which highlights how people, while taking into accounts the average of ratings for a product, still do not take care of the number of reviews leading to that average. The high impact of reviews on consumers is also testified by the fact that a positive (or negative) review about a product can be as effective as a recommendation by a friend see, e.g., https: www.brightlocal.com learn local-consumer-review-survey (All URLs accessed on June 7, 2017) . Further, positive comments convey a series of strong benefits, like, e.g., an improvement in search engines' ranking, a stronger perception of trust, and increased sales @cite_34 @cite_3 @cite_0 .
@cite_0 @cite_15 @cite_34 @cite_3
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Although extremely popular, electronic commerce environments often lack information that has traditionally served to ensure trust among exchange partners. Digital technologies, however, have created new forms of "electronic word-of-mouth," which offer new potential for gathering credible information that guides consumer behaviors. We conducted a nationally representative survey and a focused experiment to assess how individuals perceive the credibility of online commercial information, particularly as compared to information available through more traditional channels, and to evaluate the specific aspects of ratings information that affect people's attitudes toward ecommerce. Survey results show that consumers rely heavily on web-based information as compared to other channels, and that ratings information is critical in the evaluation of the credibility of online commercial information. Experimental results indicate that ratings are positively associated with perceptions of product quality and purchase intention, but that people attend to average product ratings, but not to the number of ratings or to the combination of the average and the number of ratings together. Thus suggests that in spite of valuing the web and ratings as sources of commercial information, people use ratings information suboptimally by potentially privileging small numbers of ratings that could be idiosyncratic. In addition, product quality is shown to mediate the relationship between user ratings and purchase intention. The practical and theoretical implications of these findings are considered for ecommerce scholars, consumers, and vendors. 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. The development of Web 2.0 technology has led to huge economic benefits and challenges for both e-commerce websites and online shoppers. One core technology to increase sales and consumers' satisfaction is the use of recommender systems. Existing product recommender systems consider the order of items purchased by users to obtain a list of recommended items. However, they do not consider the time interval between the products purchased. For example, there is often an interval of 2-3 months between the purchase of printer ink cartridges or refills. Thus, recommending appropriate ink cartridges one week before the user needs to replace the depleted ink cartridges would increase the likelihood of a purchase decision. In this paper, we propose to utilize the purchase interval information to improve the performance of the recommender systems for e-commerce. We design an efficient algorithm to compute the purchase intervals between product pairs from users' purchase history and integrate this information into the marginal utility model. We evaluate our approach on a real world ecommerce dataset. Experimental results demonstrate that our approach significantly improves the conversion rate and temporal diversity compared to state-of-the-art algorithms. With the rapid growth of the Internet, users' ability to publish content has created active electronic communities that provide a wealth of product information. Consumers naturally gravitate to reading reviews in order to decide whether to buy a product. However, the high volume of reviews that are typically published for a single product makes it harder for individuals to locate the best reviews and understand the true underlying quality of a product based on the reviews. Similarly, the manufacturer of a product needs to identify the reviews that influence the customer base, and examine the content of these reviews. In this paper, we propose two ranking mechanisms for ranking product reviews: a consumer-oriented ranking mechanism ranks the reviews according to their expected helpfulness, and a manufacturer-oriented ranking mechanism ranks the reviews according to their expected effect on sales. Our ranking mechanism combines econometric analysis with text mining techniques in general, and with subjectivity analysis in particular. We show that subjectivity analysis can give useful clues about the helpfulness of a review and about its impact on sales. Our results can have several implications for the market design of online opinion forums.