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A comparative assessment of sentiment analysis and star ratings for consumer reviews

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  • Al-Natour, Sameh
  • Turetken, Ozgur

Abstract

Electronic word of mouth (eWOM) is prominent and abundant in consumer domains. Both consumers and product/service providers need help in understanding and navigating the resulting information spaces, which are vast and dynamic. The general tone or polarity of reviews, blogs or tweets provides such help. In this paper, we explore the viability of automatic sentiment analysis (SA) for assessing the polarity of a product or a service review. To do so, we examine the potential of the major approaches to sentiment analysis, along with star ratings, in capturing the true sentiment of a review. We further model contextual factors (specifically, product type and review length) as two moderators affecting SA accuracy. The results of our analysis of 900 reviews suggest that different tools representing the main approaches to SA display differing levels of accuracy, yet overall, SA is very effective in detecting the underlying tone of the analyzed content, and can be used as a complement or an alternative to star ratings. The results further reveal that contextual factors such as product type and review length, play a role in affecting the ability of a technique to reflect the true sentiment of a review.

Suggested Citation

  • Al-Natour, Sameh & Turetken, Ozgur, 2020. "A comparative assessment of sentiment analysis and star ratings for consumer reviews," International Journal of Information Management, Elsevier, vol. 54(C).
    • Handle: RePEc:eee:ininma:v:54:y:2020:i:c:s0268401219311181
    DOI: 10.1016/j.ijinfomgt.2020.102132
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    Cited by:

    1. Ransome Epie Bawack & Samuel Fosso Wamba & Kevin Daniel André Carillo & Shahriar Akter, 2022. "Artificial intelligence in E-Commerce: a bibliometric study and literature review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 297-338, March.
    2. Enrique Bigne & Carla Ruiz & Carmen Perez-Cabañero & Antonio Cuenca, 2023. "Are customer star ratings and sentiments aligned? A deep learning study of the customer service experience in tourism destinations," Service Business, Springer;Pan-Pacific Business Association, vol. 17(1), pages 281-314, March.
    3. Xue, Lan & Leung, Xi Y. & Ma, Shihan (David), 2022. "What makes a good “guest”: Evidence from Airbnb hosts' reviews," Annals of Tourism Research, Elsevier, vol. 95(C).
    4. Nan Yang & Nikolaos Korfiatis & Dimitris Zissis & Konstantina Spanaki, 2024. "Incorporating topic membership in review rating prediction from unstructured data: a gradient boosting approach," Annals of Operations Research, Springer, vol. 339(1), pages 631-662, August.
    5. Sambit Tripathi & Amit V. Deokar & Haya Ajjan, 2022. "Understanding the Order Effect of Online Reviews: A Text Mining Perspective," Information Systems Frontiers, Springer, vol. 24(6), pages 1971-1988, December.
    6. Yujia Liu & Jihui Li, 2024. "An Online Hotel Selection Method With Three-Dimensional Analysis of Reviews' Helpfulness," International Journal of Fuzzy System Applications (IJFSA), IGI Global, vol. 13(1), pages 1-25, January.
    7. Jia-Lang Xu & Ying-Lin Hsu, 2022. "The Impact of News Sentiment Indicators on Agricultural Product Prices," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1645-1657, April.
    8. Md Shamim Hossain & Mst Farjana Rahman, 2023. "Customer Sentiment Analysis and Prediction of Insurance Products’ Reviews Using Machine Learning Approaches," FIIB Business Review, , vol. 12(4), pages 386-402, December.
    9. Lu, Lin & Xu, Pei & Wang, Yen-Yao & Wang, Yu, 2023. "Measuring service quality with text analytics: Considering both importance and performance of consumer opinions on social and non-social online platforms," Journal of Business Research, Elsevier, vol. 169(C).

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