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.

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.

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.

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 BUYER RATING AND SENTIMENT ANALYSIS USING DISCRIMINATORY SUPPORT VECTOR MACHINE (SVM)

2021 ◽  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Opinion Mining ◽  
Marketing Strategies ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Product Reviews ◽  
Purchase Decisions ◽  
Customer Reviews ◽  
Customer Feedback

This substantial issue is increasingly important in business and culture. It presents many challenging research scenarios but guarantees a relevant insight for everybody interested in view evaluation and social networking analysis. This paper's key aim is to detect sentiment polarity such as positive, negative, and emoji representation with customer feedback on various products. Opinion mining from e-commerce sites has a significant part in making purchase decisions and founders to boost their product and marketing strategies. But, it becomes very difficult for the clients to understand and assess the product's actual view manually. Because of this, we need an automated way. The majority of the researchers used machine learning algorithms to do an automated representation of phrase embedding. Among the popular techniques in machine learning has been used the support vector machine (SVM). The weighted support vector machine (WSVM) is the improved version for the standard SVM to grow the outlier sensitivity issue. In this paper, the word2Vec version uses to extract the attributes from customer reviews in WSVM based on opinion analysis of product reviews in E-commerce websites. The experiment result shows that the suggested WSVM can works better on the opinion classification job doing any version applied.

Do online consumer reviews help to evaluate the performance of automobile manufacturers?

2020 ◽  
Vol 33 (5) ◽  
pp. 1153-1198
Author(s):  
Amit Singh ◽  
Mamata Jenamani ◽  
Jitesh Thakkar
Keyword(s):  
Sentiment Analysis ◽  
Data Set ◽  
Pareto Chart ◽  
Content Type ◽  
Customer Reviews ◽  
Online Consumer Reviews ◽  
Online Customer Reviews ◽  
Fishbone Diagram ◽  
Online Consumer ◽  
Practical Implications

PurposeThis research proposes a text analytics–based framework that examines the utility of online customer reviews in evaluating automobile manufacturers and discovering their consumer-perceived weaknesses.Design/methodology/approachThe proposed framework integrates aspect-level sentiment analysis with the house of quality (HoQ), TOPSIS, Pareto chart and fishbone diagram. While sentiment analysis mines and quantifies review-embedded consumer opinions on various automobile attributes, the integrated HoQ-TOPSIS analyzes the quantified opinions and evaluates the manufacturers. The Pareto charts assist in discovering consumer-perceived weaknesses of the underperforming manufacturers. Finally, the fishbone diagram visually represents the results in the form with which the manufacturing community is acquainted.FindingsThe proposed framework is tested on a review data set collected from CarWale, a well-known car portal in India. Selecting five manufacturers from the mid-size car segment, the authors identified the worst-performing one and discovered its weak attributes.Practical implicationsThe proposed framework can help the manufacturers in evaluating competitor; identifying consumers' contemporary interests; discovering own and their competitors' weak attributes; assessing the suppliers and sending early warnings; detecting the hazardous defects. It can assist the component suppliers in devising process improvement strategies; improving their customer network; comparing them with competitors. It can support the customers in identifying the best available alternative.Originality/valueThe proposed framework is first of its kind to integrate the sentiment analysis with (1) HoQ-TOPSIS to assess the manufacturers; (2) Pareto chart to discover their weaknesses; (3) fishbone diagram to visually represent the results.

Sentimental Analysis of Online Reviews Using Fuzzy Sets and Rough Sets

2017 ◽  
pp. 376-394
Author(s):  
Anuradha Jagadeesan ◽  
Amit Patil
Keyword(s):  
Fuzzy Sets ◽  
Sentiment Analysis ◽  
Rough Sets ◽  
Opinion Mining ◽  
Online Reviews ◽  
Purchase Decisions ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
Excellent Source ◽  
The Web

With the increased interest of online users in E-commerce, the web has become an excellent source for buying and selling of products online. Customer reviews on the web help potential customers to make purchase decisions, and for manufacturers to incorporate improvements in their product or develop new marketing strategies. The increase in customer reviews of a product influence the popularity and the sale rate of the product. This lead to a very important question about the analysis of the sentiments (opinions) expressed in the reviews. As such internet does not have any quality control over customer reviews and it could vary in terms of its quality. Also the trustworthiness of the online reviews is debatable. Sentiment Analysis (SA) or Opinion Mining is the computational analysis of opinions, sentiments, emotions and subjectivity of text. In this chapter, we take a look at the various research challenges and a new dimension involved in sentiment analysis using fuzzy sets and rough sets.

Aspect-level opinion mining of online customer reviews

2013 ◽  
Vol 10 (3) ◽  
pp. 25-41 ◽  
Author(s):  
Xueke Xu ◽  
Xueqi Cheng ◽  
Songbo Tan ◽  
Yue Liu ◽  
Huawei Shen
Keyword(s):  
Opinion Mining ◽  
Customer Reviews ◽  
Online Customer Reviews

Predicting Future Importance of Product Features Based on Online Customer Reviews

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
Huimin Jiang ◽  
C. K. Kwong ◽  
K. L. Yung
Keyword(s):  
Time Series ◽  
Opinion Mining ◽  
Fuzzy Inference ◽  
Online Reviews ◽  
Fuzzy Time Series ◽  
Series Method ◽  
Customer Reviews ◽  
Product Features ◽  
Online Customer Reviews ◽  
Time Series Method

Previous studies conducted customer surveys based on questionnaires and interviews, and the survey data were then utilized to analyze product features. In recent years, online customer reviews on products became extremely popular, which contain rich information on customer opinions and expectations. However, previous studies failed to properly address the determination of the importance of product features and prediction of their future importance based on online reviews. Accordingly, a methodology for predicting future importance weights of product features based on online customer reviews is proposed in this paper which mainly involves opinion mining, a fuzzy inference method, and a fuzzy time series method. Opinion mining is adopted to analyze the online reviews and extract product features. A fuzzy inference method is used to determine the importance weights of product features using both frequencies and sentiment scores obtained from opinion mining. A fuzzy time series method is adopted to predict the future importance of product features. A case study on electric irons was conducted to illustrate the proposed methodology. To evaluate the effectiveness of the fuzzy time series method in predicting the future importance, the results obtained by the fuzzy time series method are compared with those obtained by the three common forecasting methods. The results of the comparison show that the prediction results based on fuzzy time series method are better than those based on exponential smoothing, simple moving average, and fuzzy moving average methods.

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.

Sign in / Sign up

Export Citation Format

Share Document