Feature-Based Opinion Classification Using the KPCA Technique: Concept and Performance Evaluation

Over the last several years, a widespread trend on the internet has been the proliferation of online evaluations written by people with whom they share their ideas, interests, experiences, and opinions. Opinion mining, also known as sentiment analysis, is the process of classifying pieces of text written in a natural language on a subject into positive, negative, or neutral categories according to the human emotions, views, and feelings that are communicated in that text. The field of sentiment analysis has progressed to the point that it can now analyse internet evaluations and provide significant information to people as well as corporations, which may assist these parties in the decision-making process. In the proposed model, feature extraction extracts the collection of features that are both semantically and statistically significant using the kernel principal component analysis (KPCA) method. According to the findings of the simulations, the suggested model performs better than other existing models

Sustainable lighting product development underpinned by online data mining and life cycle assessment

The accurate acquisition of customer requirement information is an important part in product planning and positioning, it plays a decisive role in the success of products in the market. the rapid development of e-commerce makes increasing more consumers shopping online and a big volume of customer reviews are posted on different Websites. The online reviews contain valuable opinions of customers, enabling designers to understand their concerns. In this research, an integrated approach has been developed to mine customer requirements according to the online reviews collected from e-commerce sites to form product design specifications. The main research contents include the following aspects: (1) development of useful online review prediction and classification approach; (2) online review implicit product features and sentiment analysis based on the constructed feature and sentiment lexicon; (3) built a knowledge base containing customer requirements mined from online reviews; (4) conduct a dedicated environmental and social LCA on the proposed domestic lighting product by using a professional LCA software. In this study, multiple models and technologies/methods have been successfully implemented: review helpfulness classification model has been constructed based on the training set and test set by tuning and optimizing; proposes a new approach to implicit feature and sentiment analysis, based on explicit formal feature-emotion sentences, implicit feature sentences and implicit sentiment sentences, combined with a feature lexicon, a 1V1/1Vn sentiment-feature rule base and the feature-emotion word pairs are extracted; based on the preliminary analysis results of feature extraction and sentiment analysis, combined with KANO model to establish user requirement mining rules, and consider satisfaction, propose the user demand priority to obtain the final list of user requirements; a real industrial context with lighting product manufacturer (ONA) in Spain has involved with the lighting product life cycle analysis and development for new product. The analytical results of these studies present an in-depth modelling and analysis on the sustainable lighting product lifecycle with the aid of real manufacturing data

Sentiment Analysis for Online Product Reviews and Recommendation Using Deep Learning Based Optimization Algorithm

Recently, online shopping is becoming a popular means for users to buy and consume with the advances in Internet technologies. Satisfaction of users could be efficiently improvised by carrying out a Sentiment Analysis (SA) of larger amount of user reviews on e-commerce platform. But still, it is a challenge to envision the precise sentiment polarity of the user reviews due to the modifications in sequence length, complicated logic, and textual order. In this study, we propose a Hybrid-Flash Butterfly Optimization with Deep Learning based Sentiment Analysis (HFBO-DLSA) for Online Product Reviews. The presented HFBO-DLSA technique mainly aims to determine the nature of sentiments based on online product reviews. For accomplishing this, the presented HFBO-DLSA technique applies data pre-processing at the preliminary stage to make it compatible. Besides, the HFBO-DLSA model uses deep belief network (DBN) model for classification. The HFBO algorithm is used as a hyperparameter tuning process to improve the SA performance of the DBN method. The experimental validation of the presented HFBO-DLSA method has been tested under a set of datasets. The experimental results reveal that the HFBO-DLSA approach surpasses recent techniques in terms of SA outcomes. Specifically, when compared to various existing models on the Canon dataset, the HFBO-DLSA technique achieves remarkable results with an accuracy of 97.66%, precision of 98.54%, recall of 94.64%, and an F-score of 96.43%. In comparative analysis, other approaches such as ACO, SVM, and NN exhibit poorer performance, while TextCNN, BiLSTM, and RCNN approaches yield slightly improved SA results

Application of Semantic Web Technologies for Supporting Customer Relationship Management: a Systematic Literature Review

The purpose of the present paper is to summarize the current state of the existing research on the application of Semantic web technologies in supporting customer relationship management. Its achievement includes providing answers to a set of research questions as a result of conducting a systematic literature review. A total of 44 scientific publications are identified as relevant to the topic and included in the review. Information is extracted from the selected literature sources, which is then summarized, systemized and analyzed according to the predefined research questions and finally reported. The conducted systematic literature review determines that the development of Semantic web technologies is provoked interest among researchers, as a result of which the advantages of using them for descriptions useful for various CRM purposes are investigated and practically confirmed. In addition to defining semantic models for descriptions supporting a variety of CRM activities and processes (such as customized products and services; supporting users of CRM systems; integrated offerings across channels; improved and innovative products and services; customer complaint management, etc.), various research works identify new approaches to support CRM, that can be achieved through the application of appropriate Semantic web technologies.The detailed study represented in this paper contributes to familiarization with the existing experience in the application of Semantic web technologies in supporting customer relationship management, as well as facilitates the discovery of trends and directions for future research. This is the reason for the expected interest from scientists whose research area cover the considered and similar fields; software engineers implementing CRM systems; data analysts exploring CRM domain.</p

Automatically Learning User Needs from Online Reviews for New Product Design

The traditional product design process begins with the identification of user needs (Ulrich and Eppinger 2008). Traditional methods for needs identification include focus groups, surveys, interviews, and anthropological studies. In this paper, we propose to augment traditional methods for identifying user needs by automatically analyzing user-generated online product reviews. Specifically, we present a supervised, machine learning approach for sentential-level adaptive text extraction and mining. Based upon a set of 9700+ digital camera product reviews gathered in January 2008, we evaluate the approach in three ways. First, we report precision and recall using n-fold cross-validation on labeled data. Second, we compare the recall of automated learning with respect to traditional measures for identifying users and their respective needs. Third, we use multi-dimensional scaling (MDS) to visualize the competitive landscape by mapping existing products in terms of the user needs that they address

An NLP-Deep Learning approach for Product Rating Prediction Based on Online Reviews and Product Features

This study focuses on predicting the popularity of a product based on its overall rating score. Unlike previous studies that focus on predicting the review rating based on sentiment analysis and polarity of the reviews, in this thesis, the effect of product features in determining its popularity is directly measured and analyzed in order to predict its overall rating score. To this end, a methodology consisting of three phases is considered. Phase 1 predicts the overall rating by feeding the general product features, extracted from the online product information available on Amazon webpages to a Deep Learning (DL) model. Phase 2 identifies other features that customers care about the most by applying the Named Entity Recognition (NER) algorithm to the customer online reviews; and lastly, Phase 3 feeds the combination of the general and custom features to the DL model to predict the overall rating score of the product. The experimental results on a dataset of laptop products, collected from Amazon, indicate an impressive performance of the proposed approach, which is mainly attributed to including custom product features to the inputs of the DL algorithm when compared with the existing method. More precisely, the proposed model could achieve the highest accuracy score of 84.01%, 84.68% for recall, 87.63% for precision, and 84.06% for F1 score. Applying this procedure could help businesses identify the specific areas of strengths and weaknesses of their products or services from the perspective of their customers, allowing them to thrive in today's competitive markets

Integration of e-business strategy for multi-lifecycle production systems

Internet use has grown exponentially on the last few years becoming a global communication and business resource. Internet-based business, or e-Business will truly affect every sector of the economy in ways that today we can only imagine. The manufacturing sector will be at the forefront of this change. This doctoral dissertation provides a scientific framework and a set of novel decision support tools for evaluating, modeling, and optimizing the overall performance of e-Business integrated multi-lifecycle production systems. The characteristics of this framework include environmental lifecycle study, environmental performance metrics, hyper-network model of integrated e-supply chain networks, fuzzy multi-objective optimization method, discrete-event simulation approach, and scalable enterprise environmental management system design. The dissertation research reveals that integration of e-Business strategy into production systems can alter current industry practices along a pathway towards sustainability, enhancing resource productivity, improving cost efficiencies and reducing lifecycle environmental impacts. The following research challenges and scholarly accomplishments have been addressed in this dissertation: Identification and analysis of environmental impacts of e-Business. A pioneering environmental lifecycle study on the impact of e-Business is conducted, and fuzzy decision theory is further applied to evaluate e-Business scenarios in order to overcome data uncertainty and information gaps; Understanding, evaluation, and development of environmental performance metrics. Major environmental performance metrics are compared and evaluated. A universal target-based performance metric, developed jointly with a team of industry and university researchers, is evaluated, implemented, and utilized in the methodology framework; Generic framework of integrated e-supply chain network. The framework is based on the most recent research on large complex supply chain network model, but extended to integrate demanufacturers, recyclers, and resellers as supply chain partners. Moreover, The e-Business information network is modeled as a overlaid hypernetwork layer for the supply chain; Fuzzy multi-objective optimization theory and discrete-event simulation methods. The solution methods deal with overall system parameter trade-offs, partner selections, and sustainable decision-making; Architecture design for scalable enterprise environmental management system. This novel system is designed and deployed using knowledge-based ontology theory, and XML techniques within an agent-based structure. The implementation model and system prototype are also provided. The new methodology and framework have the potential of being widely used in system analysis, design and implementation of e-Business enabled engineering systems

Tuning of Customer Relationship Management (CRM) via Customer Experience Management (CEM) using sentiment analysis on aspects level

This study proposes a framework that combines a supervised machine learning and a semantic orientation approach to tune Customer Relationship Management (CRM) via Customer Experience Management (CEM). The framework extracts data from social media first and then integrates CRM and CEM by tuning and optimising CRM to reflect the needs and expectations of users on social media. In other words, in order to reduce the gap between the users' predicted opinions in CRM and their opinions on social media, the existing data from CEM will be applied to determine the similar behavioural patterns of customers towards similar outcomes within CRM. CRM data and extracted data from social media will be consolidated by the unsupervised data mining method (association). The framework will lead to a quantitative approach to uncover relationships between the extracted data from social media and the CRM data. The results show that changing some aspects of the e-learning criteria that were required by students in their social media posts can help to enhance the classification accuracy in the learning management system (LMS) data and to understand more students' studying statuses. Furthermore, the results show matching between students' opinions in CRM and CEM, especially in the negative and neutral classes

Review on recent advances in information mining from big consumer opinion data for product design

In this paper, based on more than ten years' studies on this dedicated research thrust, a comprehensive review concerning information mining from big consumer opinion data in order to assist product design is presented. First, the research background and the essential terminologies regarding online consumer opinion data are introduced. Next, studies concerning information extraction and information utilization of big consumer opinion data for product design are reviewed. Studies on information extraction of big consumer opinion data are explained from various perspectives, including data acquisition, opinion target recognition, feature identification and sentiment analysis, opinion summarization and sampling, etc. Reviews on information utilization of big consumer opinion data for product design are explored in terms of how to extract critical customer needs from big consumer opinion data, how to connect the voice of the customers with product design, how to make effective comparisons and reasonable ranking on similar products, how to identify ever-evolving customer concerns efficiently, and so on. Furthermore, significant and practical aspects of research trends are highlighted for future studies. This survey will facilitate researchers and practitioners to understand the latest development of relevant studies and applications centered on how big consumer opinion data can be processed, analyzed, and exploited in aiding product design