  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.
