Horie: helpfulness of online reviews with improved embedding

Abstract

Consumer review helpfulness has a significant role in purchase decision making in an online shopping environment. Deep learning modules with pre-trained word embeddings are predominantly used to asses review helpfulness. Pre-trained word embeddings are trained on generic corpora and lack in incorporating domain knowledge and sentiment information of a word. Moreover, pre-trained embeddings fail to capture the subtle change of semantics of same word with different parts of speech. In this work, we propose HORIE (Heplfulness of Online Reviews with Improved Embedding) which improve pre-trained embedding with domain, sentiment and parts of speech information and analyse helpfulness as classification problem. In HORIE, domain knowledge is acquired from domain specific corpora. The average of pre-trained and domain specific embedding is combined with vectorized sentiment information, extracted from lexical dictionaries, along with POS tag information. Later, we apply a dual CNN based model for classification of reviews. HORIE is tested with five different domain and compare our performance with existing embeddings. We also compare our approach with handcrafted feature sets and existing helpfulness classification technique. AUROC is used as a metric. Our approach shows improvement over existing approaches.