English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper examines the effect of two-sided argumentation on the perceivedhelpfulness of online consumer reviews. In contrast to previous works, ouranalysis thereby sheds light on the reception of reviews from a language-basedperspective. For this purpose, we propose an intriguing text analysis approachbased on distributed text representations and multi-instance learning tooperationalize the two-sidedness of argumentation in review texts. A subsequentempirical analysis using a large corpus of Amazon reviews suggests thattwo-sided argumentation in reviews significantly increases their helpfulness.We find this effect to be stronger for positive reviews than for negativereviews, whereas a higher degree of emotional language weakens the effect. Ourfindings have immediate implications for retailer platforms, which can utilizeour results to optimize their customer feedback system and to present moreuseful product reviews.

Understanding the Role of Two-Sided Argumentation in Online Consumer Reviews: A Language-Based Perspective