Testing the impact of uncertainty reducing reviews in the prediction of cross domain social media pages ratings

Abstract

Purpose: To test if the factors “reviewer location” and “time frame” have any impact on the prediction results when predicting online product ratings from user reviews. Design/methodology/approach: Reviews and ratings are scraped for the product “The Secret” book through Web pages of e-commerce websites like Amazon and Flipkart. Such data is used for training the model to predict ratings of similar products based on reviews data in various other social media platforms like Facebook, Quora and YouTube. After data preprocessing, sentiment analysis is used for opinion classification. A multi-class supervised support vector machine is used for feature classification and predictions. The four models produced in the study have a prediction accuracy of 79%. The data collection is done based on a specific geographical location and specific time frame. Post evaluating the predictions, inferential statistics are used to check for significance. Findings: There will be an impact on the ratings predicted from the reviews that belong to a particular geographic location or time frame. The ratings predicted from such reviews help in taking accurate decisions as they are robust and informative. Research limitations/implications: This study is confined to a single product and for cross domain social media pages, only Facebook, YouTube and Quora data are considered. Practical implications: Provides credible ratings of a product/service on all cross domain social media pages making the initial screening process of purchase decisions better. Originality/value: Many studies explored the usefulness of reviews for rating prediction based on review nature. This study aims to identify the usefulness of reviews based on factors that would reduce uncertainty in the purchase process.