Forecasting the importance of product attributes using online customer reviews and Google Trends

2021 ◽  
Vol 171 ◽  
pp. 120983
Author(s):  
Hanan Yakubu ◽  
C.K. Kwong
Keyword(s):  
Google Trends ◽  
Product Attributes ◽  
Customer Reviews ◽  
Online Customer Reviews

Identifying Key Product Attributes and Their Importance Levels From Online Customer Reviews

2012 ◽  
Author(s):  
Rahul Rai
Keyword(s):  
Text Mining ◽  
Design Process ◽  
Product Reviews ◽  
Product Attributes ◽  
New Approach ◽  
Preference Analysis ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
Importance Ranking ◽  
The Individual

Identifying customer needs and preferences is one of the most important tasks in design process. Typically, a variation of interview based approaches is used to conduct need and preference analysis. In this paper, a new approach based on text mining online (internet based) customer reviews to supplement traditional methods of need and preference analysis is considered. The key idea underlying the proposed approach is to partition online customer generated product reviews into segments that evaluate the individual attributes of a product (e.g zoom capability and support of different image formats in a camcorder). Additionally, the proposed method also identifies the importance (ranking) that customers place on each product attributes. The method is demonstrated on 100 customer reviews submitted for camcorders on epinions.com over a two year period.

Automated Keyword Filtering in LDA for Identifying Product Attributes from Online Reviews

2020 ◽  
pp. 1-10
Author(s):  
Junegak Joung ◽  
Harrison M. Kim
Keyword(s):  
Product Design ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Online Reviews ◽  
Previous Method ◽  
Product Attributes ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
Dirichlet Allocation

Abstract Identifying product attributes from the perspective of a customer is essential to measure the satisfaction, importance, and Kano category of each product attribute for product design. This paper proposes automated keyword filtering to identify product attributes from online customer reviews based on latent Dirichlet allocation. The preprocessing for latent Dirichlet allocation is important because it affects the results of topic modeling; however, previous research performed latent Dirichlet allocation either without removing noise keywords or by manually eliminating them. The proposed method improves the preprocessing for latent Dirichlet allocation by conducting automated filtering to remove the noise keywords that are not related to the product. A case study of Android smartphones is performed to validate the proposed method. The performance of the latent Dirichlet allocation by the proposed method is compared to that of a previous method, and according to the latent Dirichlet allocation results, the former exhibits a higher performance than the latter.

Decomposing the effects of online customer reviews on brand, price, and product attributes

2016 ◽  
Vol 33 (1) ◽  
pp. 11-26 ◽  
Author(s):  
Daniel S. Kostyra ◽  
Jochen Reiner ◽  
Martin Natter ◽  
Daniel Klapper
Keyword(s):  
Product Attributes ◽  
Customer Reviews ◽  
Online Customer Reviews

Profiling Reviewers’ Social Network Strength and Predicting the “Helpfulness” of Online Customer Reviews

2020 ◽  
pp. 101026
Author(s):  
Muhammad Bilal ◽  
Mohsen Marjani ◽  
Ibrahim Abaker Targio Hashem ◽  
Nadia Malik ◽  
Muhammad Ikram Ullah Lali ◽  
...  
Keyword(s):  
Social Network ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
Network Strength

Fraud abroad

2019 ◽  
Vol 13 (2) ◽  
pp. 249-275
Author(s):  
Jake David Hoskins ◽  
Ryan Leick
Keyword(s):  
Sharing Economy ◽  
Gdp Per Capita ◽  
Content Type ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
Level Of Economic Development ◽  
Per Capita ◽  
Using Data ◽  
Developed Nations

Purpose This study aims to investigate a sharing economy context, where vacation rental units that are owned and operated by individuals throughout the world are rented out through a common website: vrbo.com. It is posited that gross domestic product (GDP) per capita, a common indicator of the level of economic development of a nation, will impact the likelihood that prospective travelers will choose to book accommodations in the sharing economy channel (vs traditional hotels). The role of online customer reviews in this process is investigated as well, building upon a significant body of extant research which shows their level of customer decision influence. Design/methodology/approach An empirical analysis is conducted using data from the website Vacation Rentals By Owner on 1,940 rental listings across 97 countries. Findings GDP per capita serves as risk deterrent to prospective travelers, making the sharing economy an acceptable alternative to traditional hotels for the average traveler. It is also found that the total number of online customer reviews (OCR volume) is a signal of popularity to prospective travelers, while the average star rating of those online customer reviews (OCR valence) is instead a signal of accommodation quality. Originality/value This study adds to a growing agenda of research investigating the effect of online customer reviews on consumer decisions, with a particularly focus on the burgeoning sharing economy. The findings help to explain when the sharing economy may serve as a stronger disruptive threat to incumbent offerings. It also provides the following key insights for managers: sharing economy rental units in developed nations are more successful in driving booking activity, managers should look to promote volume of online customer reviews and positive online customer reviews are particularly influential for sharing economy rental booking rates in less developed nations.

EXPRESS: Hype News Diffusion and Risk of Misinformation: The Oz Effect in Healthcare

2021 ◽  
pp. 002224372110444
Author(s):  
Zijun (June) Shi ◽  
Xiao Liu ◽  
Kannan Srinivasan
Keyword(s):  
Scientific Evidence ◽  
Public Information ◽  
Research Articles ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
News Agencies ◽  
Health Products ◽  
Tv Shows ◽  
Medical Recommendations ◽  
Textual Content

Consumers' choices about health products are heavily influenced by public information, such as news articles, research articles, online customer reviews, online product discussion, and TV shows. Dr. Oz, a celebrity doctor, often makes medical recommendations with limited or marginal scientific evidence. Although reputable news agencies have traditionally acted as gatekeepers of reliable information, they face the intense pressure of “the eyeball game.” Customer reviews, despite their authenticity, may come from deceived consumers. Therefore, it remains unclear whether public information sources can correct the misleading health information. In the context of over-the-counter weight loss products, the authors carefully analyze the cascading of information post endorsement. The analysis of extensive textual content with deep-learning methods reveals that legitimate news outlets respond to Dr. Oz's endorsement by generating more news articles about the ingredient; on average, articles after the endorsement contain a higher sentiment, so news agencies seem to amplify rather than rectify the misleading endorsement. The finding highlights a serious concern: the risk of hype news diffusion. Research articles react too slowly to mitigate the problem, and online customer reviews and product discussions provide only marginal corrections. The findings underscore the importance of oversight to mitigate the risk of cascading hype news.

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