Profiling users' behavior, and identifying important features of review 'helpfulness'

The increasing volume of online reviews and the use of review platforms leave tracks that can be used to explore interesting patterns. It is in the primary interest of businesses to retain and improve their reputation. Reviewers, on the other hand, tend to write reviews that can influence and attract people’s attention, which often leads to deliberate deviations from past rating behavior. Until now, very limited studies have attempted to explore the impact of user rating behavior on review helpfulness. However, there are more perspectives of user behavior in selecting and rating businesses that still need to be investigated. Moreover, previous studies gave more attention to the review features and reported inconsistent findings on the importance of the features. To fill this gap, we introduce new and modify existing business and reviewer features and propose a user-focused mechanism for review selection. This study aims to investigate and report changes in business reputation, user choice, and rating behavior through descriptive and comparative analysis. Furthermore, the relevance of various features for review helpfulness is identified by correlation, linear regression, and negative binomial regression. The analysis performed on the Yelp dataset shows that the reputation of the businesses has changed slightly over time. Moreover, 46% of the users chose a business with a minimum of 4 stars. The majority of users give 4-star ratings, and 60% of reviewers adopt irregular rating behavior. Our results show a slight improvement by using user rating behavior and choice features. Whereas, the significant increase in R2 indicates the importance of reviewer popularity and experience features. The overall results show that the most significant features of review helpfulness are average user helpfulness, number of user reviews, average business helpfulness, and review length. The outcomes of this study provide important theoretical and practical implications for researchers, businesses, and reviewers

Enhancing Knowledge and Bounce Rate in SERPs Using Micro-Data

Internet has revolutionized the human life. SEs (Search Engines) are one of the major tools being used for finding information over the Internet. SEs enlist the information into links as per relevance to the searched query. A searcher usually visits the top web links retrieved on SERPs (Search Engine Results Pages) in response to a search query. With the evolving nature of Internet and the increasing number of competitors; it is hard to maintain high ranking in SERPs even for professional correspondents. However, correspondents can apply the techniques of web micro-data to achieve high CTR (Click through Rate) in SERPs. Ranking in major SEs is still a critical factor, although in certain cases such as movies, books, recipes rich snippets proved profitable for webmasters. This study aims to address the gap in micro-data moving from top category such as Animals to their limited scope. Animals with information such as name, price, category will have high CTR and hence more user satisfaction for specified result will lead to high ranking in SERPs