Natural Language Processing of Laundry Detergent Online Reviews Reveals Brand-Specific Consumer Experience Patterns and the Dominance of Smell

2021 ◽  
Author(s):  
Ioannis Kontaris ◽  
Philip Ian Nicholas Ulrich
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Laundry Detergent ◽  
Online Reviews ◽  
Consumer Experience

Design of GA and Ontology based NLP Frameworks for Online Opinion Mining

2019 ◽  
Vol 13 (2) ◽  
pp. 159-165
Author(s):  
Manik Sharma ◽  
Gurvinder Singh ◽  
Rajinder Singh
Keyword(s):  
Genetic Algorithm ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Opinion Mining ◽  
Hybrid Genetic Algorithm ◽  
Online Reviews ◽  
Middle Tier ◽  
Complete Set ◽  
Mining Model

Background: For almost every domain, a tremendous degree of data is accessible in an online and offline mode. Billions of users are daily posting their views or opinions by using different online applications like WhatsApp, Facebook, Twitter, Blogs, Instagram etc. Objective: These reviews are constructive for the progress of the venture, civilization, state and even nation. However, this momentous amount of information is useful only if it is collectively and effectively mined. Methodology: Opinion mining is used to extract the thoughts, expression, emotions, critics, appraisal from the data posted by different persons. It is one of the prevailing research techniques that coalesce and employ the features from natural language processing. Here, an amalgamated approach has been employed to mine online reviews. Results: To improve the results of genetic algorithm based opining mining patent, here, a hybrid genetic algorithm and ontology based 3-tier natural language processing framework named GAO_NLP_OM has been designed. First tier is used for preprocessing and corrosion of the sentences. Middle tier is composed of genetic algorithm based searching module, ontology for English sentences, base words for the review, complete set of English words with item and their features. Genetic algorithm is used to expedite the polarity mining process. The last tier is liable for semantic, discourse and feature summarization. Furthermore, the use of ontology assists in progressing more accurate opinion mining model. Conclusion: GAO_NLP_OM is supposed to improve the performance of genetic algorithm based opinion mining patent. The amalgamation of genetic algorithm, ontology and natural language processing seems to produce fast and more precise results. The proposed framework is able to mine simple as well as compound sentences. However, affirmative preceded interrogative, hidden feature and mixed language sentences still be a challenge for the proposed framework.

Validating Perceived Sustainable Design Features Using a Novel Collage Approach

2021 ◽  
pp. 1-46
Author(s):  
Nasreddine El Dehaibi ◽  
Ting Liao ◽  
Erin F. MacDonald
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Sustainable Design ◽  
Design Method ◽  
Online Reviews ◽  
Design Features ◽  
Sustainable Products ◽  
Low Correlation ◽  
Social Environmental

Abstract Designers are challenged to create sustainable products that resonate with customers, often focusing on engineered sustainability while neglecting perceived sustainability. We previously proposed a method for extracting perceived sustainable features from online reviews using annotations and natural language processing, testing our method with French press coffee carafes. We identified that perceived sustainability may not always align with engineered sustainability. We now investigate how designers can validate perceived features extracted from online reviews using a relatively new design method of collage placement where participants drag and drop products on a collage and select features from a drop-down menu. We created collage activities for participants to evaluate French press products on the three aspects of sustainability: social, environmental, and economic, and on how much they like the products. During the activity participants placed products along the two axes of the collage, sustainability and likeability, and labeled products with descriptive features. We found that participants more often selected our previously extracted features when placing products higher on the sustainability axis, validating that the perceived sustainable features resonate with users. We also measured a low correlation between the two-axes of the collage activity, indicating that perceived sustainability and likeability can be measured separately. In addition, we found that product perceptions across sustainability aspects may differ between demographics. Based on these results, we confirm that the collage is an effective tool for validating sustainability perceptions and that features perceived as sustainable from online reviews resonate with customers when thinking of various sustainability aspects.

Validating Perceived Sustainable Design Features Using a Novel Collage Approach

2021 ◽  
Author(s):  
Nasreddine El-Dehaibi ◽  
Ting Liao ◽  
Erin F. MacDonald
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Sustainable Design ◽  
Design Method ◽  
Online Reviews ◽  
Design Features ◽  
Customer Perceptions ◽  
Product Features ◽  
Social Environmental

Abstract Fierce e-commerce competition challenges designers to differentiate their products on platforms such as Amazon. To achieve this differentiation, designers must first understand how customers perceive product features. This paper builds on our previous work where we extracted features perceived as sustainable for French Press coffee carafes using annotations of Amazon reviews and natural language processing (NLP). We identified a gap between customer perceptions of sustainability and engineered sustainability. We now test our findings with a relatively new design method of collage placement and investigate how designers can use perceived features to set their products apart. We created collage activities for participants to evaluate French Press products on the three aspects of sustainability: social, environmental, and economic, and on how much they like the products. During the activity participants placed products along the two axes of the collage, sustainability and likeability, and labeled products with descriptive features that we provided. We found that participants more often selected features perceived as sustainable when placing products higher on the sustainability axis, demonstrating that these features resonated with customers. We also measured a low correlation between the two-axes of the collage activity, indicating that perceived sustainability and likeability can be measured separately. In addition, we found that product perceptions across sustainability aspects may differ between demographics. Based on these results, we confirm that features perceived as sustainable that are extracted from online reviews resonate with customers when thinking of various sustainability aspects and that the collage is an effective tool for assessing sustainability perceptions.

Analysis of Online Reviews of Orthopaedic Surgeons and Orthopaedic Practices Using Natural Language Processing

2020 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
David W. G. Langerhuizen ◽  
Laura E. Brown ◽  
Job N. Doornberg ◽  
David Ring ◽  
Gino M. M. J. Kerkhoffs ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Online Reviews ◽  
Orthopaedic Surgeons

Customers' experience of purchasing event tickets: mining online reviews based on topic modeling and sentiment analysis

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Krzysztof Celuch
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Topic Modeling ◽  
Data Science ◽  
Latent Dirichlet Allocation ◽  
Online Reviews ◽  
Third Party ◽  
Content Type

PurposeIn search of creating an extraordinary experience for customers, services have gone beyond the means of a transaction between buyers and sellers. In the event industry, where purchasing tickets online is a common procedure, it remains unclear as to how to enhance the multifaceted experience. This study aims at offering a snapshot into the most valued aspects for consumers and to uncover consumers' feelings toward their experience of purchasing event tickets on third-party ticketing platforms.Design/methodology/approachThis is a cross-disciplinary study that applies knowledge from both data science and services marketing. Under the guise of natural language processing, latent Dirichlet allocation topic modeling and sentiment analysis were used to interpret the embedded meanings based on online reviews.FindingsThe findings conceptualized ten dimensions valued by eventgoers, including technical issues, value of core product and service, word-of-mouth, trustworthiness, professionalism and knowledgeability, customer support, information transparency, additional fee, prior experience and after-sales service. Among these aspects, consumers rated the value of the core product and service to be the most positive experience, whereas the additional fee was considered the least positive one.Originality/valueDrawing from the intersection of natural language processing and the status quo of the event industry, this study offers a better understanding of eventgoers' experiences in the case of purchasing online event tickets. It also provides a hands-on guide for marketers to stage memorable experiences in the era of digitalization.

scholarly journals The Challenges of Modeling and Predicting Online Review Helpfulness

2021 ◽  
Author(s):  
Rogério Figueredo de Sousa ◽  
Thiago Alexandre Salgueiro Pardo
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Online Reviews ◽  
Important Task ◽  
Online Review ◽  
Huge Amount ◽  
Review Helpfulness

Predicting review helpfulness is an important task in Natural Language Processing. It is useful for dealing with the huge amount of online reviews on varied domains and languages, helping and guiding users on what to read and consider in their daily decisions. However, there are limited initiatives to investigate the nature of this task and how hard it is. This paper aims to fulfill this gap, providing a better understanding of it. Two complementary experiments are performed in order to uncover patterns of usefulness evaluation as performed by humans and relevant features for machine prediction. To assure our results, we run the experiments for two different domains: movies and apps. We show that humans agree on the process of assigning helpfulness to reviews, despite the difficulty of the task. More than this, people perform this process systematically and consistently. Finally, we empirically identify the most relevant content features for machine learning prediction of review helpfulness.

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