id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-020905-gw8i6tkn	Qu, Xianshan	An Attention Model of Customer Expectation to Improve Review Helpfulness Prediction	2020-03-17		.txt	text/plain	5412	330	60	To model such customer expectations and capture important information from a review text, we propose a novel neural network which leverages review sentiment and product information. In order to address the above issues, we propose a novel neural network architecture to introduce sentiment and product information when identifying helpful content from a review text. In the cold start scenario, our proposed model demonstrates an AUC improvement of 5.4% and 1.5% on Amazon and Yelp data sets, respectively, when compared to the state of the art model. From Table 5 , we see that adding a sentiment attention layer (HSA) to the base model (HBiLSTM) results in an average improvement in the AUC score of 2.0% and 2.6%, respectively on the Amazon and Yelp data sets. In this paper, we describe our analysis of review helpfulness prediction and propose a novel neural network model with attention modules to incorporate sentiment and product information.	./cache/cord-020905-gw8i6tkn.txt	./txt/cord-020905-gw8i6tkn.txt