Predicting the “helpfulness” of online consumer reviews

By Jyoti Prakash Singh, Seda Irani, Nripendra P. Rana, Yogesh Kumar Dwivedi, Sunil Saumya, Pradeep Kumar Roy. 2017

400 citations recorded by the source corpus.

Online shopping is increasingly becoming people's first choice when shopping, as it is very convenient to choose products based on their reviews. Even for moderately popular products, there are thousands of reviews constantly being posted on e-commerce sites. Such a large volume of data constantly being generated can be considered as a big data challenge for both online businesses and consumers. That makes it difficult for buyers to go through all the reviews to make purchase decisions. In this research, we have developed models based on machine learning that can predict the helpfulness of the consumer reviews using several textual features such as polarity, subjectivity, entropy, and reading ease. The model will automatically assign helpfulness values to an initial review as soon as it is posted on the website so that the review gets a fair chance of being viewed by other buyers. The results of this study will help buyers to write better reviews and thereby assist other buyers in making their purchase decisions, as well as help businesses to improve their websites.

Subfields: Artificial Intelligence, Information Systems and Management, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/5e2332cbd95bfc217ebf39002fe5135a9d589d3b

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Citations in the corpus

Consumer responses to functional, aesthetic and symbolic product design in online reviews (2017, background): “Given the increasing pervasiveness of online reviews (Singh et al., 2017), examining online word-of-mouth (WOM) behaviors from a consumer perspective that focuses on the content of online reviews rather than reviewer characteristics is timely.”
Setting the future of digital and social media marketing research: Perspectives and research propositions (2020, neutral): “…et al., 2015b, Abed et al., 2016; Dwivedi et al., 2015a; Felix et al., 2017; Kapoor et al., 2016; Plume et al., 2016; Rathore et al., 2016; Shareef et al., 2018; Shareef et al., 2019a; Shareef, Mukerji, Dwivedi, Rana, & Islam, 2019b; Shiau et al., 2017, 2018; Singh et al., 2017; Yang et al., 2017).”
Perceived helpfulness of eWOM: Emotions, fairness and rationality (2020, background): “However, information overload from vast quantities of eWOM can cause confusion and result in a negative effect on purchase intention (Furner & Zinko, 2017; Singh et al., 2017).”
A survey on sentiment analysis methods, applications, and challenges (2022, background): “Later the list is expanded by looking at the antonyms and synonyms in the available lexical resources (Singh et al. 2017; Ho et al. 2014).”