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.

Testing the SmartMunk's Story.ly App for the Analysis of Online Customer Reviews on Face Masks

2021 ◽  
pp. 90-116
Author(s):  
Arabela Briciu ◽  
Cristian-Laurențiu Roman ◽  
Victor-Alexandru Briciu
Keyword(s):  
Text Analysis ◽  
User Satisfaction ◽  
Product Reviews ◽  
Online Application ◽  
Customer Reviews ◽  
Customer Feedback ◽  
Specialized Software ◽  
Online Customer Reviews ◽  
Qualitative Part ◽  
Software Measures

This chapter aims to present the process of selecting and analyzing a number of reviews using a software solution (an online application) created specifically for text analysis and extracting user sentiment. This software measures the level of user satisfaction, analyzing product reviews and taking into account the qualitative part of the content generated by users. Analyzing online customer reviews with the help of specialized software can help both companies and other users. The software can also help us reach a conclusion regarding the analysis of reviews and customer feedback on products or services. This study can also be useful for customers or buyers who want to know the opinion of others about a product, having the opportunity to differentiate between positive and negative reviews.

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

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.

A Unique Method of Constructing Brand Perceptual Maps by the Text Mining of Multimedia Consumer Reviews

2018 ◽  
Vol 9 (3) ◽  
pp. 1-22
Author(s):  
Amir Ekhlassi ◽  
Amirhosein Zahedi
Keyword(s):  
Opinion Mining ◽  
Product Reviews ◽  
Customer Reviews ◽  
Perceptual Maps ◽  
Online Customer Reviews ◽  
Visual Technique ◽  
Unique Method ◽  
Online Consumer ◽  
The Mind ◽  
Digital Tablets

Brand perceptual mapping is a visual technique, it displays how a brand is positioned in the mind of customers, as well as in relation to the competitors. With the rapid growth of e-commerce and the abundance of online consumer-generated content, there is no need for marketers to go through market research in order to understand consumers' opinions. Therefore, in this study, the authors propose a unique method which allows the building of a perceptual map automatically by mining consumer opinions from in particular online product reviews. The authors employ opinion mining techniques to extract and rank the product aspects that are important to customers, during purchasing digital tablets. Subsequently, they generate a score for each brand in these aspects and build the perceptual map using clustering of the brands by these scores. This proposed method is applied to the online customer reviews for digital tablets obtained from Amazon.com. The experimental results highlight the proposed technique is effective and able to correctly depict the position of a brand in its particular competitive environment.

Mining opinionated product features using WordNet lexicographer files

2016 ◽  
Vol 43 (6) ◽  
pp. 769-785 ◽  
Author(s):  
Saif A. Ahmad Alrababah ◽  
Keng Hoon Gan ◽  
Tien-Ping Tan
Keyword(s):  
Assessment Tool ◽  
Empirical Evaluation ◽  
Online Reviews ◽  
Product Reviews ◽  
Specific Domain ◽  
Customer Reviews ◽  
Product Features ◽  
Online Customer Reviews ◽  
Customer Perspective ◽  
Massive Number

Online customer reviews are an important assessment tool for businesses as they contain feedback that is valuable from the customer perspective. These reviews provide a significant basis on which potential customers can select the product that best meets their preferences. In online reviews, customers describe positive or negative experiences with a product or service or any part of it (i.e. features). Consumers frequently experience difficulty finding the desired product for comparison because of the massive number of online reviews. The automatic extraction of important product features is necessary to support customers in search of relevant product features. These features are the criteria that make it possible for customers to characterise different types of products. This article proposes a domain independent approach for identifying explicit opinionated features and attributes that are strongly related to a specific domain product using lexicographer files in WordNet. In our approach, N_gram analysis and the SentiStrength opinion lexicon have been employed to support the extraction of opinionated features. The empirical evaluation of the proposed system using online reviews of two popular datasets of supervised and unsupervised systems showed that our approach achieved competitive results for feature extraction from product reviews.

scholarly journals Definition of customer requirements in big data using word vectors and affinity propagation clustering

2021 ◽  
pp. 095440892110017
Author(s):  
Yanlin Shi ◽  
Qingjin Peng
Keyword(s):  
Big Data ◽  
Product Design ◽  
Affinity Propagation ◽  
Product Reviews ◽  
Customer Requirements ◽  
Parts Of Speech ◽  
Customer Reviews ◽  
Affinity Propagation Clustering ◽  
Online Customer Reviews ◽  
Definition Of

Customer requirements (CRs) have a significant impact on product design. The existing methods of defining CRs, such as customer surveys and expert evaluations, are time-consuming, inaccurate and subjective. This paper proposes an automatic CRs definition method based on online customer product reviews using the big data analysis. Word vectors are defined using a continuous bag of words (CBOW) model. Online customer reviews are searched by a crawling method and filtered by the parts of speech and frequency of words. Filtered words are then clustered into groups by an affinity propagation (AP) clustering method based on trained word vectors. Exemplars in each clustering group are finally used to define CRs. The proposed method is verified by case studies of defining CRs for product design. Results show that the proposed method has better performance to determine CRs compared to existing CRs definition methods.

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