A comparative assessment of sentiment analysis and star ratings for consumer reviews. (October 2020)

• Record Type: Journal Article
• Authors: Al-Natour, Sameh; Turetken, Ozgur
• Is Part Of: International journal of information management. Volume 54(2020)
• Publication Date: 2020-10
• Subjects: Sentiment analysis -- eWOM -- Consumer reviews -- Machine-learning -- Comparative assessment; Social sciences -- Information services -- Periodicals; Social sciences -- Research -- Periodicals; Information science -- Periodicals; Management information systems -- Periodicals; Knowledge management -- Periodicals; Sciences sociales -- Documentation, Services de -- Périodiques; Sciences sociales -- Recherche -- Périodiques; Sciences de l'information -- Périodiques; Systèmes d'information de gestion -- Périodiques; Information science; Management information systems; Social sciences -- Information services; Social sciences -- Research; Periodicals; Electronic journals; 025.52068
• DOI: 10.1016/j.ijinfomgt.2020.102132
• Languages: English
• ISSNs: 0268-4012

Abstract

Highlights: Sentiment analysis can be used to determine the general tone of a consumer review. We compare the output of several SA tools to each other and to star ratings. Results show that SA can substitute for, and often outperforms, star ratings. Contextual factors, such as product type, moderate SA accuracy. 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.