scholarly journals Methodology for Applying Text Mining Techniques to Analyzing Online Customer Reviews for Market Segmentation

2009 ◽  
Vol 9 (8) ◽  
pp. 272-284 ◽  
Author(s):  
Keun-Hyung Kim ◽  
Sung-Ryoel Oh
Keyword(s):  
Text Mining ◽  
Market Segmentation ◽  
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.

Application of social media analytics: a case of analyzing online hotel reviews

2017 ◽  
Vol 41 (7) ◽  
pp. 921-935 ◽  
Author(s):  
Wu He ◽  
Xin Tian ◽  
Ran Tao ◽  
Weidong Zhang ◽  
Gongjun Yan ◽  
...  
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Social Media Analytics ◽  
Social Media Data ◽  
Content Type ◽  
Customer Reviews ◽  
Analysis Techniques ◽  
Online Customer Reviews ◽  
Media Data

Purpose Online customer reviews could shed light into their experience, opinions, feelings, and concerns. To gain valuable knowledge about customers, it becomes increasingly important for businesses to collect, monitor, analyze, summarize, and visualize online customer reviews posted on social media platforms such as online forums. However, analyzing social media data is challenging due to the vast increase of social media data. The purpose of this paper is to present an approach of using natural language preprocessing, text mining and sentiment analysis techniques to analyze online customer reviews related to various hotels through a case study. Design/methodology/approach This paper presents a tested approach of using natural language preprocessing, text mining, and sentiment analysis techniques to analyze online textual content. The value of the proposed approach was demonstrated through a case study using online hotel reviews. Findings The study found that the overall review star rating correlates pretty well with the sentiment scores for both the title and the full content of the online customer review. The case study also revealed that both extremely satisfied and extremely dissatisfied hotel customers share a common interest in the five categories: food, location, rooms, service, and staff. Originality/value This study analyzed the online reviews from English-speaking hotel customers in China to understand their preferred hotel attributes, main concerns or demands. This study also provides a feasible approach and a case study as an example to help enterprises more effectively apply social media analytics in practice.

Text mining approach to explore dimensions of airline customer satisfaction using online customer reviews

2020 ◽  
Vol 83 ◽  
pp. 101760 ◽  
Author(s):  
Filipe R. Lucini ◽  
Leandro M. Tonetto ◽  
Flavio S. Fogliatto ◽  
Michel J. Anzanello
Keyword(s):  
Text Mining ◽  
Customer Satisfaction ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document