Online reviews are increasingly becoming a helpful resource for customers in their purchase decisions. Their helpfulness appears to be an important asset to evaluate the effectiveness of online reviews. Based on elaboration likelihood model (ELM), this study focuses on the factors in online consumer reviews that can influence review helpfulness and how the impact generated by these factors varies according to product type. Several analytical processes are applied to gather information on review content, such as feature extraction, sentiment analysis, and emotion analysis. An analysis of 1,673 reviews from Amazon.com shows that rating, length, image count, polarity, anger, fear, joy, and trust in reviews affect review helpfulness positively while subjectivity, informativeness, anticipation, sadness, and surprise in reviews have negative influence on review helpfulness. Product type is found to moderate the impact of review length, image count, review subjectivity, review informativeness, and emotions such as sadness, disgust, and joy on review helpfulness.
Kurtcan et al. (Thu,) studied this question.
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