Understanding the Role of Two-Sided Argumentation in Online Consumer Reviews: A Language-Based Perspective
This research addresses the problem of optimizing customer feedback systems for retailer platforms, though it is incremental as it builds on existing work with a language-based perspective.
The paper investigates how two-sided argumentation in online consumer reviews affects perceived helpfulness, finding that it significantly increases helpfulness, especially for positive reviews, while emotional language weakens the effect.
This paper examines the effect of two-sided argumentation on the perceived helpfulness of online consumer reviews. In contrast to previous works, our analysis thereby sheds light on the reception of reviews from a language-based perspective. For this purpose, we propose an intriguing text analysis approach based on distributed text representations and multi-instance learning to operationalize the two-sidedness of argumentation in review texts. A subsequent empirical analysis using a large corpus of Amazon reviews suggests that two-sided argumentation in reviews significantly increases their helpfulness. We find this effect to be stronger for positive reviews than for negative reviews, whereas a higher degree of emotional language weakens the effect. Our findings have immediate implications for retailer platforms, which can utilize our results to optimize their customer feedback system and to present more useful product reviews.