Voice of the Customer Oriented New Product Synthesis Over Knowledge Graphs

2018 ◽  
Author(s):  
Feiwei Qin ◽  
Hairui Xu ◽  
Weicheng Zhang ◽  
Lin Yuan ◽  
Ming Li ◽  
...  
Keyword(s):  
Language Processing ◽  
Online Shopping ◽  
Specific Aspect ◽  
Product Reviews ◽  
Product Knowledge ◽  
Online Data ◽  
Challenges And Opportunities ◽  
Knowledge Graphs ◽  
The Voice ◽  
Voice Of The Customer

The online shopping has been much easier and popular, and meanwhile brings new challenges and opportunities to the field of product design and marketing sale. On one hand, product manufacturers find it challenging to produce new popularly accepted products to meet the customers’ needs; on the other hand, end customers usually feel it difficult to buy ideal goods that they really want, even if navigating a huge amount of commodities. There are indeed a ‘communication gap’ between the customers and manufacturers. As an effort to partially resolve the issue, this paper proposes a novel product synthesis approach from ‘voice of the customer’ over product knowledge graphs. Here the voice of customers mainly refer to the buyers’ product reviews from online shopping platforms or blogs, while the product knowledge graph is constructed containing professional hierarchical product knowledge on its properties based on ontological models. Using the technologies of natural language processing, we first extract the customs’ polarities on each specific aspect of a product, which are then transited to design requirements on the product’s design components. Based on the requirement extractions, and the pre-built product knowledge, semantic web and reasoning techniques are utilized to synthesize a novel product that meets more customer needs. Typical case studies on mobile phones from raw online data demonstrate the proposed approach’s performance.

scholarly journals A Survey: Sentimental Analysis on Product Reviews Using (MLT) Machine Learning Techniques and Approaches

2021 ◽  
Vol 10 (2) ◽  
pp. 1253-1263
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Machine Learning Techniques ◽  
Main Task ◽  
Product Reviews ◽  
Online Data ◽  
Comparative Performance ◽  
Classical Text

Social media sites are used today for the development of different types and nature of customers those use such benefits which are often shared by people on social media symbolic or textual opinions, ideas, and feelings. This attitude and orientation draw attention to research and analyze sentiments through online data about customer interest. Therefore, the sentimental analysis idea is proposed. This is among the various uses of Natural Language Processing (NLP) and Machine Learning Analysis (MLA) is very common. The main task of sentimental analysis is the classification of sentiments automatically into three categories that are positive, negative and neutral. Many classification researches are conducted over the years to know the exact feelings and situations of sentimental emotions of people. Classification, fuzzy and clustering, is used. To know the sentiment analysis of the people’s accurate feeling and situation, many times over the years classification research was conducted in past. The accuracy of classification is finding more in Fuzzy based. Fuzzy based classification finds more accurate and for comparative study execution Classical Text Classifications Model is used. In comparative performance, this study shows the possibility of implementing the proposed method able to provide more accurate results when it comes in comparison with conventional classifiers. In this article we have discussed different researchers worked on the method of sentiment analysis and classification. This article also shows the importance of extracting comments and analyze sentiments

scholarly journals World Café method integrated with QFD for obtaining the Voice of the Customer

Production ◽  
2018 ◽  
Vol 28 (0) ◽  
Author(s):  
Jair Gustavo de Mello Torres ◽  
Pedro Luiz de Oliveira Costa Neto
Keyword(s):  
World Cafe ◽  
The Voice ◽  
Voice Of The Customer

scholarly journals Geostatistical Learning: Challenges and Opportunities

2021 ◽  
Vol 7 ◽  
Author(s):  
Júlio Hoffimann ◽  
Maciel Zortea ◽  
Breno de Carvalho ◽  
Bianca Zadrozny
Keyword(s):  
Statistical Learning ◽  
Spatial Correlation ◽  
Language Processing ◽  
Real Data ◽  
Model Errors ◽  
Process Data ◽  
Applied Machine Learning ◽  
Geophysical Surveys ◽  
Challenges And Opportunities ◽  
Geophysical Processes

Statistical learning theory provides the foundation to applied machine learning, and its various successful applications in computer vision, natural language processing and other scientific domains. The theory, however, does not take into account the unique challenges of performing statistical learning in geospatial settings. For instance, it is well known that model errors cannot be assumed to be independent and identically distributed in geospatial (a.k.a. regionalized) variables due to spatial correlation; and trends caused by geophysical processes lead to covariate shifts between the domain where the model was trained and the domain where it will be applied, which in turn harm the use of classical learning methodologies that rely on random samples of the data. In this work, we introduce the geostatistical (transfer) learning problem, and illustrate the challenges of learning from geospatial data by assessing widely-used methods for estimating generalization error of learning models, under covariate shift and spatial correlation. Experiments with synthetic Gaussian process data as well as with real data from geophysical surveys in New Zealand indicate that none of the methods are adequate for model selection in a geospatial context. We provide general guidelines regarding the choice of these methods in practice while new methods are being actively researched.

scholarly journals PENGARUH FAKTOR INTERNAL DAN EKSTERNAL TERHADAP SIKAP DAN NIAT PEMBELIAN DARING

2017 ◽  
Vol 19 (2) ◽  
pp. 192 ◽  
Author(s):  
Lorentia Shierly ◽  
Sabrina Sihombing
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Sampling Method ◽  
Online Shopping ◽  
Web Design ◽  
External Factors ◽  
Equation Modeling ◽  
Online Data ◽  
Internal Factors ◽  
Main Factors

Online shopping has grown popularity over the years because of its convenient and can often save money for buyers. There are two main factors that can affect a person to do shopping online: internal factors and external factors. Previous research often focuses on one factor only, that is, internal or external factors in influencing online shopping. Therefore, this research attempts to integrate both internal (i.e., personal innovation and hedonic motivation) and external factors (i.e., web design and eWOM) in predicting attitude and intention to buy online. Data was collected by using questionnaires with non-probability sampling method. The number of respondents was 228 respondents. Data was then analyzed by Structural Equation Modeling (SEM). Results showed that four out of five hypotheses are supported. Specifically, the results showed that personal innovation is not a significant predictor of attitude toward online shopping. This study also provides research limitations and suggestions for further research.

scholarly journals Text analysis on health product reviews using r approach

2020 ◽  
Vol 18 (3) ◽  
pp. 1303
Author(s):  
Nasibah Husna Mohd Kadir ◽  
Sharifah Aliman
Keyword(s):  
Language Processing ◽  
Text Summarization ◽  
Unstructured Data ◽  
Product Reviews ◽  
Health Product ◽  
Text Analytics ◽  
Data Set ◽  
Web Based ◽  
The Social ◽  
Key Techniques

In the social media, product reviews contain of text, emoticon, numbers and symbols that hard to identify the text summarization. Text analytics is one of the key techniques in exploring the unstructured data. The purpose of this study is solving the unstructured data by sort and summarizes the review data through a Web-Based Text Analytics using R approach. According to the comparative table between studies in Natural Language Processing (NLP) features, it was observed that Web-Based Text Analytics using R approach can analyze the unstructured data by using the data processing package in R. It combines all the NLP features in the menu part of the text analytics process in steps and it is labeled to make it easier for users to view all the text summarization. This study uses health product review from Shaklee as the data set. The proposed approach shows the acceptable performance in terms of system features execution compared with the baseline model system.

scholarly journals Analisis Pencarian Informasi Remaja Generasi z dalam Proses Pengambilan Keputusan Belanja Online (Analisis pada Mahasiswa Ilmu Komunikasi Universitas Tidar)

2020 ◽  
Vol 4 (2) ◽  
pp. 146
Author(s):  
Fitria Khairum Nisa ◽  
Arief Bregas Viratama ◽  
Nurul Hidayanti
Keyword(s):  
Social Media ◽  
Information Search ◽  
Online Shopping ◽  
Internet Usage ◽  
Search Process ◽  
Product Reviews ◽  
Sources Of Information ◽  
Digital World ◽  
Generation Z ◽  
Depth Interviews

<p><strong>Abstrak<br /></strong></p><p><strong></strong>Berdasarkan <em>survey</em>, salah kegiatan berinternet yang paling sering dilakukan adalah belanja <em>online</em> sebanyak 44.6%. Sedangkan generasi yang mendominasi penggunaan internet adalah generasi z. Generasi z merupakan generasi yang akrab dengan dunia digital dan berani mengambil resiko. Penelitian ini bertujuan untuk melihat bagaimana generasi z melakukan proses pencarian informasi dalam melakukan belanja <em>online</em><em> </em>dengan menyebarkan angket serta melakukan wawancara mendalam. Penelitian ini menggunakan <em>mix method. </em>Adapun subjek dari penelitian ini adalah mahasiswa Program Studi Ilmu Komunikasi, Universitas Tidar. Hasil penelitian ini menunjukan bahwa ulasan produk di <em>e-commerce</em> merupakan sumber utama remaja generasi z dalam mencari informasi yakni sebesar 80.7%. Sumber informasi lainnya adalah ulasan produk di sosial media dan bertanya kepada teman. Adapun alasannya adalah untuk mencari <em>trend</em> terkini serta ulasan produk dapat dipercaya dan generasi z peduli dengan pendapat orang sekitar.</p><p> </p><p><em><strong>Abstract</strong></em></p><p><em>Based on the survey, one of the most frequently carried out internet activities was online shopping which is 44.6%. Meanwhile, the generation that dominates internet usage is z generation. Z generation z is a generation that is familiar with the digital world and is willing to take risks. This study aims to see how generation Z performs the information search process in online shopping. This study uses a mix method by distributing questionnaires and conducting in-depth interviews. Subject of this research is the students of communication in Tidar University. The results of this study indicate that product reviews on e-commerce is the main source of z generation adolescents in seeking information, which is 80.7%. Other sources of information are product reviews on social media and asking friends. The reasons for those are they look for the newest trend and product reviews are trustworthy and z generation cares what people think about them.</em></p>

QFD: Echoing the Voice of the Customer

1991 ◽  
Vol 70 (2) ◽  
pp. 18-32 ◽  
Author(s):  
Patrick G. Brown
Keyword(s):  
The Voice ◽  
Voice Of The Customer

BUILD KNOWLEDGE GRAPH FROM HETEROGENEOUS DOCUMENTS

2021 ◽  
Vol 47 (05) ◽  
Author(s):  
NGUYỄN CHÍ HIẾU
Keyword(s):  
Information Retrieval ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Semantic Analysis ◽  
Knowledge Graph ◽  
Question Answering Systems ◽  
Knowledge Graphs

Knowledge Graphs are applied in many fields such as search engines, semantic analysis, and question answering in recent years. However, there are many obstacles for building knowledge graphs as methodologies, data and tools. This paper introduces a novel methodology to build knowledge graph from heterogeneous documents.  We use the methodologies of Natural Language Processing and deep learning to build this graph. The knowledge graph can use in Question answering systems and Information retrieval especially in Computing domain

Societal Change and Wisdom: Insights from the World after Covid Project

2021 ◽  
Author(s):  
Igor Grossmann ◽  
Oliver Twardus ◽  
Michael E. W. Varnum ◽  
Eranda Jayawickreme ◽  
John McLevey
Keyword(s):  
Social Sciences ◽  
Language Processing ◽  
Mixed Method ◽  
Societal Change ◽  
Psychological Science ◽  
The World ◽  
Challenges And Opportunities ◽  
Societal Changes ◽  
Time Capsule ◽  
The Common

How will the world change as a result of the Covid-19 pandemic? What can people do to best adapt to the societal changes ahead? To answer these questions, over the course of the summer-fall 2020 we launched the World After COVID Project, interviewing more than 50 of the world’s leading scholars in the behavioral and social sciences, including fellows of national academies and presidents of major scientific societies. Experts independently shared their thoughts on what effects the COVID-19 pandemic will have on our societies and provided advice for successful response to new challenges and opportunities. Using mixed-method and natural language processing analyses, we distilled and analyzed these predictions and suggestions, observing a diversity of scenarios. Results also show that half of the experts approach their post-Covid predictions dialectically, highlighting both positive and negative features of the same prediction. Moreover, prosocial goals and meta-cognition—two chief tenants of the Common Wisdom model—were evident in their recommendations for how to cope with possible changes. The project provides a time capsule of experts’ predictions during major societal changes. We discuss implications for strengthening focus on prediction (vs. mere explanation) in psychological science as well as the value of uncertainty and dialecticism in forecasting.

The case for NLP-enhanced database tuning

2021 ◽  
Vol 14 (7) ◽  
pp. 1159-1165
Author(s):  
Immanuel Trummer
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Large Body ◽  
Prototype System ◽  
Web Documents ◽  
Natural Language Text ◽  
Body Of Knowledge ◽  
Challenges And Opportunities ◽  
Automated Tuning ◽  
Database Tuning

A large body of knowledge on database tuning is available in the form of natural language text. We propose to leverage natural language processing (NLP) to make that knowledge accessible to automated tuning tools. We describe multiple avenues to exploit NLP for database tuning, and outline associated challenges and opportunities. As a proof of concept, we describe a simple prototype system that exploits recent NLP advances to mine tuning hints from Web documents. We show that mined tuning hints improve performance of MySQL and Postgres on TPC-H, compared to the default configuration.

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