Identifying Features and Predicting Consumer Helpfulness of Product Reviews

Major corporations utilize data from online platforms to make user product or service recommendations. Companies like Netflix, Amazon, Yelp, and Spotify rely on purchasing trends, user reviews, and helpfulness votes to make content recommendations. This strategy can increase user engagement on a company\u27s platform. However, misleading and/or spam reviews significantly hinder the success of these recommendation strategies. The rise of social media has made it increasingly difficult to distinguish between authentic content and advertising, leading to a burst of deceptive reviews across the marketplace. The helpfulness of the review is subjective to a voting system. As such, this study aims to predict product reviews that are helpful and enable strategies to moderate a user review post to improve the helpfulness quality of a review. The prediction of review helpfulness will utilize NLP methods against Amazon product review data. Multiple machine learning principles of different complexities will be implemented in this review to compare the results and ease of implementation (e.g., Naïve Bayes and BERT) to predict a product review\u27s helpfulness. The result of this study concludes that review helpfulness can be effectively predicted through the deployment of model features. The removal of duplicate reviews, the imputing of review helpfulness based on word count, and the inclusion of lexical elements are recommended to be included in review analysis. The results of this research indicate that the deployment of these features results in a high F1-Score of 0.83 for predicting helpful Amazon product reviews

A new evolution equation

We propose a new evolution equation for the gluon density relevant for the region of small $x_B$. It generalizes the GLR equation and allows deeper penetration in dense parton systems than the GLR equation does. This generalization consists of taking shadowing effects more comprehensively into account by including multigluon correlations, and allowing for an arbitrary initial gluon distribution in a hadron. We solve the new equation for fixed $\alpha_s$. We find that the effects of multigluon correlations on the deep-inelastic structure function are small.Comment: 29 papes Latex. 5 figs

¿Por qué no hay más casos de Insider Trading en Argentina?: una aproximación

Esta tesis solo está en formato papel por lo que se debe consultar en la propia Biblioteca Di Tella. La consulta se hace solo bajo reserva escribiendo a [email protected]

Identifying Features and Predicting Consumer Helpfulness of Product Reviews

Major corporations utilize data from online platforms to make user product or service recommendations. Companies like Netflix, Amazon, Yelp, and Spotify rely on purchasing trends, user reviews, and helpfulness votes to make content recommendations. This strategy can increase user engagement on a company\u27s platform. However, misleading and/or spam reviews significantly hinder the success of these recommendation strategies. The rise of social media has made it increasingly difficult to distinguish between authentic content and advertising, leading to a burst of deceptive reviews across the marketplace. The helpfulness of the review is subjective to a voting system. As such, this study aims to predict product reviews that are helpful and enable strategies to moderate a user review post to improve the helpfulness quality of a review. The prediction of review helpfulness will utilize NLP methods against Amazon product review data. Multiple machine learning principles of different complexities will be implemented in this review to compare the results and ease of implementation (e.g., Naïve Bayes and BERT) to predict a product review\u27s helpfulness. The result of this study concludes that review helpfulness can be effectively predicted through the deployment of model features. The removal of duplicate reviews, the imputing of review helpfulness based on word count, and the inclusion of lexical elements are recommended to be included in review analysis. The results of this research indicate that the deployment of these features results in a high F1-Score of 0.83 for predicting helpful Amazon product reviews

TIGAR Deficiency Blunts Angiotensin-II-Induced Cardiac Hypertrophy in Mice

Hypertension is the key contributor to pathological cardiac hypertrophy. Growing evidence indicates that glucose metabolism plays an essential role in cardiac hypertrophy. TP53-induced glycolysis and apoptosis regulator (TIGAR) has been shown to regulate glucose metabolism in pressure overload-induced cardiac remodeling. In the present study, we investigated the role of TIGAR in cardiac remodeling during Angiotensin II (Ang-II)-induced hypertension. Wild-type (WT) and TIGAR knockout (KO) mice were infused with Angiotensin-II (Ang-II, 1 µg/kg/min) via mini-pump for four weeks. The blood pressure was similar between the WT and TIGAR KO mice. The Ang-II infusion resulted in a similar reduction of systolic function in both groups, as evidenced by the comparable decrease in LV ejection fraction and fractional shortening. The Ang-II infusion also increased the isovolumic relaxation time and myocardial performance index to the same extent in WT and TIGAR KO mice, suggesting the development of similar diastolic dysfunction. However, the knockout of TIGAR significantly attenuated hypertension-induced cardiac hypertrophy. This was associated with higher levels of fructose 2,6-bisphosphate, PFK-1, and Glut-4 in the TIGAR KO mice. Our present study suggests that TIGAR is involved in the control of glucose metabolism and glucose transporters by Ang-II and that knockout of TIGAR attenuates the development of maladaptive cardiac hypertrophy