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Review helpfulness in online settings: an analysis of informational and emotional content

Betul Durkaya Kurtcan & Sebnem Burnaz

International Journal of Electronic Marketing and Retailing2026https://doi.org/10.1504/ijemr.2026.151735article
ABDC C
Weight
0.50

What the paper says

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.

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Cite this paper

https://doi.org/https://doi.org/10.1504/ijemr.2026.151735

Or copy a formatted citation

@article{betul2026,
  title        = {{Review helpfulness in online settings: an analysis of informational and emotional content}},
  author       = {Betul Durkaya Kurtcan & Sebnem Burnaz},
  journal      = {International Journal of Electronic Marketing and Retailing},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1504/ijemr.2026.151735},
}

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Review helpfulness in online settings: an analysis of informational and emotional content

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.