Framework for Sentiment-Driven Evaluation of Customer Satisfaction With Cosmetics Brands

Park, Jaehun

doi:10.1109/access.2020.2997522

Cited by 22 publications

(12 citation statements)

References 55 publications

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“…Ref. [18] developed and presented a framework for cosmetics consumers' satisfaction with cosmetics brands, including the products' quality. Moreover, young adult consumers are regarded as the most significant consumer segment in relation to ethical and sustainable consumption [19], which suggests that cosmetics producers and retailers should focus more on the ethical attributes of their products and the ethical behaviour of cosmetics suppliers [20].…”

Section: Quality: Ethical Sources and Consumer Experiencesmentioning

confidence: 99%

The Impact of Communicating Sustainability and Ethical Behaviour of the Cosmetic Producers: Evidence from Thailand

Yamaoka

2022

Sustainability

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Purpose—This study aims to demonstrate the antecedent factors of consumers’ cosmetics purchasing behaviour in the emerging market of Thailand from the perspective of sustainability. Specifically, the study aims to quantitatively analyse the impact of three hypothesised antecedents of consumer behaviour: product quality, communicating sustainability, and ethical business behaviour. Methodology—A quantitative methodology is applied in the study, which collects survey data from Thailand. This study focuses on two cosmetic brands in Thailand, a domestic brand and an international brand. The total 800-sample dataset was analysed using Structural Equation Modelling to validate a conceptual model with measurements of three antecedent factors: quality, ethical behaviour, and communication sustainability. Findings—It is found that ‘ethical behaviour of the producers had a non-significant impact for all samples and the ‘domestic brand’, whereas communicating sustainability had a significant impact in all sample cases. The proposed measurement scales present a practical and pioneering tool for assessing consumer responses and behaviour towards cosmetic brands. The set of scales will also help cosmetics marketers to appraise their strategic planning and monitor their progress toward creating and identifying consumer loyalty to cosmetics brands via producers’ ethical behaviour and CSR messaging. Originality—The global market and inter-stakeholder communications have greatly changed the way people perceive, behave towards, and react to business suppliers. The understanding of consumer brand loyalty in the cosmetics industry and the business strategies focused on the impact of communicating sustainability with ethical behaviour remain limited, especially in the context of the emerging market. This research contributes to filling this gap with empirical analyses.

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Section: Quality: Ethical Sources and Consumer Experiencesmentioning

confidence: 99%

The Impact of Communicating Sustainability and Ethical Behaviour of the Cosmetic Producers: Evidence from Thailand

Yamaoka

2022

Sustainability

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“…Sentimental analysis of beauty product reviews in social media is the motivating research in this paper. The influence of social media reviews on beauty products has a positive impact on choosing the right product [3]. But to lead the marketplace, the brands may influence the marketing in the review comments.…”

Section: -Proposed Frameworkmentioning

confidence: 99%

“…They are very beneficial for consumers to help in making decisions regarding the specific product [2]. Presently, the communication conduct of this digital era"s customers has been customized towards the beauty industry that developed as a highly competitive business market [3]. Various social media and e-commerce platforms provide reviews and ratings of different types and brands of cosmetics products to consumers.…”

Section: -Introductionmentioning

confidence: 99%

A Hybrid Machine Learning Approach for Sentiment Analysis of Beauty Products Reviews

Jindal¹,

Aron²

2022

jist

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Nowadays, social media platforms have become a mirror that imitates opinions and feelings about any specific product or event. These product reviews are capable of enhancing communication among entrepreneurs and their customers. These reviews need to be extracted and analyzed to predict the sentiment polarity, i.e., whether the review is positive or negative. This paper aims to predict the human sentiments expressed for beauty product reviews extracted from Amazon and improve the classification accuracy. The three phases instigated in our work are data pre-processing, feature extraction using the Bag-of-Words (BoW) method, and sentiment classification using Machine Learning (ML) techniques. A Global Optimization-based Neural Network (GONN) is proposed for the sentimental classification. Then an empirical study is conducted to analyze the performance of the proposed GONN and compare it with the other machine learning algorithms, such as Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM). We dig further to cross-validate these techniques by ten folds to evaluate the most accurate classifier. These models have also been investigated on the Precision-Recall (PR) curve to assess and test the best technique. Experimental results demonstrate that the proposed method is the most appropriate method to predict the classification accuracy for our defined dataset. Specifically, we exhibit that our work is adept at training the textual sentiment classifiers better, thereby enhancing the accuracy of sentiment prediction.

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“…Cosmetic brands apply sentiment analysis to obtain a clear and comprehensive insight into consumers’ thoughts on product quality and desires. In [ 13 ], Park implemented Term Frequency–Inverse Document Frequency to analyze the polarity of customer opinions and brand satisfaction for 26 different cosmetic companies. The research also focused on the factors affecting the nature of consumers’ views.…”

Section: Related Workmentioning

confidence: 99%

Statistics-Based Outlier Detection and Correction Method for Amazon Customer Reviews

Chatterjee

Zhou

Abusorrah

et al. 2021

Entropy

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People nowadays use the internet to project their assessments, impressions, ideas, and observations about various subjects or products on numerous social networking sites. These sites serve as a great source to gather data for data analytics, sentiment analysis, natural language processing, etc. Conventionally, the true sentiment of a customer review matches its corresponding star rating. There are exceptions when the star rating of a review is opposite to its true nature. These are labeled as the outliers in a dataset in this work. The state-of-the-art methods for anomaly detection involve manual searching, predefined rules, or traditional machine learning techniques to detect such instances. This paper conducts a sentiment analysis and outlier detection case study for Amazon customer reviews, and it proposes a statistics-based outlier detection and correction method (SODCM), which helps identify such reviews and rectify their star ratings to enhance the performance of a sentiment analysis algorithm without any data loss. This paper focuses on performing SODCM in datasets containing customer reviews of various products, which are (a) scraped from Amazon.com and (b) publicly available. The paper also studies the dataset and concludes the effect of SODCM on the performance of a sentiment analysis algorithm. The results exhibit that SODCM achieves higher accuracy and recall percentage than other state-of-the-art anomaly detection algorithms.

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Framework for Sentiment-Driven Evaluation of Customer Satisfaction With Cosmetics Brands

Cited by 22 publications

References 55 publications

The Impact of Communicating Sustainability and Ethical Behaviour of the Cosmetic Producers: Evidence from Thailand

The Impact of Communicating Sustainability and Ethical Behaviour of the Cosmetic Producers: Evidence from Thailand

A Hybrid Machine Learning Approach for Sentiment Analysis of Beauty Products Reviews

Statistics-Based Outlier Detection and Correction Method for Amazon Customer Reviews

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