User review helpfulness assessment based on sentiment analysis

Abstract

<jats:sec> <jats:title content-type="abstract-subheading">Purpose</jats:title> <jats:p>This paper aims to investigate the relationship between sentiment and review helpfulness and develop a method to fully use sentiment features in review helpfulness assessment. In addition, this paper explores whether product type influences evaluating review helpfulness.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title> <jats:p>First, a high-quality data set with a manually coded helpfulness score was constructed. Second, detailed research question methods were conducted. Finally, methods were applied to the data set to extract information gain and sentiment scores. Gradient boosting and random forest methods were used to classify the data set with these features through recall, precision and F-measure to understand the research questions.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Findings</jats:title> <jats:p>Review sentiment has a deep relationship with review helpfulness, and it can be a strong predictor of review helpfulness by refining it into more detailed scores; a combination of sentiment scores and information gain works very well on classification for two product types. Product type does not show a significant influence on helpfulness assessment.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Originality/value</jats:title> <jats:p>This paper provides a different perspective for measuring review sentiment by clarifying the relationship between sentiment and review helpfulness, analysing the role of product type in review helpfulness assessment, and proposing a high-value feature combination. In addition, the author believes that the assessment method can be effectively applied to practical works.</jats:p> </jats:sec>

Profiling Users’ Behavior, and Identifying Important Features of Review “Helpfulness”

“…The sentiment of online reviews has been reported as a strong predictor of review helpfulness. In addition, the product type did not show any significant impact on the helpfulness of online reviews [48], [49]. A study examined the impact of different features, i.e.…”

Predicting the future increment of review helpfulness: an empirical study based on a two-wave data set

“…In addition, many studies have been focusing on distinguishing sentimental opinions and statements of fact (objective statements) from online reviews. Some studies believe that affective words, both in terms of emotional variety and intensity, could increase the helpfulness of reviews (Choi, 2019; Martin et al , 2014; Zeng et al , 2020). Some argue that the helpfulness of reviews increases when the review has a larger proportion of negative words (Baek et al , 2012) because they believe that people feel normative pressure to speak of only positive things, and thus those who talk about negative things are recognized to have a persuasive effect (Ito et al , 1998).…”

Discovery of Product Features for Redesign from User Implicit Feedback

“…Sentiment features are another influential factor that is strongly correlated with review helpfulness prediction [33,34,42,43]. Sentiment features reveal the customer experience of products, mainly including negative, positive, and neutral emotional tendencies [30,34].…”

“…Sentiment features are another influential factor that is strongly correlated with review helpfulness prediction [33,34,42,43]. Sentiment features reveal the customer experience of products, mainly including negative, positive, and neutral emotional tendencies [30,34]. rough the analysis of negative and positive features, manufacturers can evaluate customer needs and preferences about their products [28,33].…”