| dc.description.abstract |
Helpful online reviews crave the attention of many researchers as it significantly affects purchase decision. However, consumers’ perception of helpfulness remains an open problem due to a lack of semantic analysis of review content and unreliable voting mechanism. In this work, we propose three qualitative perspectives considering both semantic and syntactic features of review content - lexical, sequential and structural to assess helpfulness. N-gram based semantic relation among words is explored with a d-CNN model, to predict helpfulness from lexical perspective. Sequential perspective is analysed with LSTM model, which predict helpfulness by comprehending sequence of words. Structural perspective is addressed with fourteen syntactic statistical features and predict helpfulness of review. These three models of qualitative perspective trained with “X of Y” ratio of helpfulness voting. Now, to decimate the unreliability of helpfulness voting mechanism and unveil the human perception of helpfulness, the manual scoring approach is implemented over a sample of reviews. With experimentation, we show that there exists a linear relationship among the perspectives with the human perceived helpfulness score. It is observed that all these perspectives have an impact on consumers’ perception of helpfulness of a review. Five different product category of a benchmark dataset has been used for experimentation. A sample of 2000 reviews from five different categories has been used for human scoring of helpfulness. Finally, we estimate the weights of each of the perspectives of consumers’ perception of helpfulness from online reviews and discuss the significant theoretical and practical implications. |
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