A Conceptual Framework for Enhancing Product Search with Product Information from Reviews

Product search today is limited, as users can only search and filter for a restricted set of product features, e.g. 15” and 1TB hard disk when searching for a laptop. The often decision- critical aspects of a product are however hidden in user reviews (“noisy fan” or “bright display”) and are not available until a product has been found. This paper proposes a conceptual framework for the integration of product aspects, that have been mined and derived from consumer reviews, into the product search. The framework structures the challenges that arise in four major fields and gives an overview of existing research for each one of them: Data challenges, user experience challenges, purchase process challenges and business challenges. It may inform researchers from various disciplines to perform target-oriented research as well as practitioners what to consider when building up such an enriched product search

Aspect Βased Classification Model for Social Reviews

Aspect based opinion mining investigates deeply, the emotions related to one’s aspects. Aspects and opinion word identification is the core task of aspect based opinion mining. In previous studies aspect based opinion mining have been applied on service or product domain. Moreover, product reviews are short and simple whereas, social reviews are long and complex. However, this study introduces an efficient model for social reviews which classifies aspects and opinion words related to social domain. The main contributions of this paper are auto tagging and data training phase, feature set definition and dictionary usage. Proposed model results are compared with CR model and Naïve Bayes classifier on same dataset having accuracy 98.17% and precision 96.01%, while recall and F1 are 96.00% and 96.01% respectively. The experimental results show that the proposed model performs better than the CR model and Naïve Bayes classifier

A Context-Dependent Sentiment Analysis of Online Product Reviews based on Dependency Relationships

Consumers often view online consumer product review as a main channel for obtaining product quality information. Existing studies on product review sentiment analysis usually focus on identifying sentiments of individual reviews as a whole, which may not be effective and helpful for consumers when purchase decisions depend on specific features of products. This study proposes a new feature-level sentiment analysis approach for online product reviews. The proposed method uses an extended PageRank algorithm to extract product features and construct expandable context-dependent sentiment lexicons. Moreover, consumers’ sentiment inclinations toward product features expressed in each review can be derived based on term dependency relationships. The empirical evaluation using consumer reviews of two different products shows a higher level of effectiveness of the proposed method for sentiment analysis in comparison to two existing methods. This study provides new research and practical insights on the analysis of online consumer product reviews

Product Redesign and Innovation Based on Online Reviews:A Multistage Combined Search Method

Online reviews published on the e-commerce platform provide a new source of information for designers to develop new products. Past research on new product development (NPD) using user-generated textual data commonly focused solely on extracting and identifying product features to be improved. However, the competitive analysis of product features and more specific improvement strategies have not been explored deeply. This study fully uses the rich semantic attributes of online review texts and proposes a novel online review–driven modeling framework. This new approach can extract fine-grained product features; calculate their importance, performance, and competitiveness; and build a competitiveness network for each feature. As a result, decision making is assisted, and specific product improvement strategies are developed for NPD beyond existing modeling approaches in this domain. Specifically, online reviews are first classified into redesign- and innovation-related themes using a multiple embedding model, and the redesign and innovation product features can be extracted accordingly using a mutual information multilevel feature extraction method. Moreover, the importance and performance of features are calculated, and the competitiveness and competitiveness network of features are obtained through a personalized unidirectional bipartite graph algorithm. Finally, the importance performance competitiveness analysis plot is constructed, and the product improvement strategy is developed via a multistage combined search algorithm. Case studies and comparative experiments show the effectiveness of the proposed method and provide novel business insights for stakeholders, such as product providers, managers, and designers

MELex: a new lexicon for sentiment analysis in mining public opinion of Malaysia affordable housing projects

Sentiment analysis has the potential as an analytical tool to understand the preferences of the public. It has become one of the most active and progressively popular areas in information retrieval and text mining. However, in the Malaysia context, the sentiment analysis is still limited due to the lack of sentiment lexicon. Thus, the focus of this study is to a new lexicon and enhance the classification accuracy of sentiment analysis in mining public opinion for Malaysia affordable housing project. The new lexicon for sentiment analysis is constructed by using a bilingual and domain-specific sentiment lexicon approach. A detailed review of existing approaches has been conducted and a new bilingual sentiment lexicon known as MELex (Malay-English Lexicon) has been generated. The developed approach is able to analyze text for two most widely used languages in Malaysia, Malay and English, with better accuracy. The process of constructing MELex involves three activities: seed words selection, polarity assignment and synonym expansions, with four different experiments have been implemented. It is evaluated based on the experimentation and case study approaches where PR1MA and PPAM are selected as case projects. Based on the comparative results over 2,230 testing data, the study reveals that the classification using MELex outperforms the existing approaches with the accuracy achieved for PR1MA and PPAM projects are 90.02% and 89.17%, respectively. This indicates the capabilities of MELex in classifying public sentiment towards PRIMA and PPAM housing projects. The study has shown promising and better results in property domain as compared to the previous research. Hence, the lexicon-based approach implemented in this study can reflect the reliability of the sentiment lexicon in classifying public sentiments

Discovering the value of unstructured data in business settings

With the increasing amount of unstructured data in business settings, the analysis of unstructured data is reshaping business practices in many industries. The implementation of unstructured data analysis will eventually have dominant presence in all department of organisations thus contributing to the organisations. This dissertation focuses the most widely utilised unstructured data-textual data within the organisation. A variety of techniques has been applied in three studies to discover the information within the unstructured textual data. Study I proposed a dynamic model that incorporates values from topic membership, an outcome variable from Latent Dirichlet Allocation (a probabilistic topic model), with sentiment analysis for rating prediction. A variety of machine learning algorithms are employed to validate the model. Study II focused on the exploration of online reviews from customers in the OFD domain. In addition, this study examines the outcomes of franchising in the service sector from the customer’s perspective. This study identifies key issues during the processes of producing and delivering product/services from service providers to customers in service industries using a large-scale dataset. Study III extends the data scope to the firm-level data. Latent signals are discovered from companies’ self-descriptions. In addition, the association between the signals and the organisation context of the entrepreneurship is also examined, which could display the heterogeneity of various signals across different organisation context

A semantic metadata enrichment software ecosystem (SMESE) : its prototypes for digital libraries, metadata enrichments and assisted literature reviews

Contribution 1: Initial design of a semantic metadata enrichment ecosystem (SMESE) for Digital Libraries The Semantic Metadata Enrichments Software Ecosystem (SMESE V1) for Digital Libraries (DLs) proposed in this paper implements a Software Product Line Engineering (SPLE) process using a metadata-based software architecture approach. It integrates a components-based ecosystem, including metadata harvesting, text and data mining and machine learning models. SMESE V1 is based on a generic model for standardizing meta-entity metadata and a mapping ontology to support the harvesting of various types of documents and their metadata from the web, databases and linked open data. SMESE V1 supports a dynamic metadata-based configuration model using multiple thesauri. The proposed model defines rules-based crosswalks that create pathways to different sources of data and metadata. Each pathway checks the metadata source structure and performs data and metadata harvesting. SMESE V1 proposes a metadata model in six categories of metadata instead of the four currently proposed in the literature for DLs; this makes it possible to describe content by defined entity, thus increasing usability. In addition, to tackle the issue of varying degrees of depth, the proposed metadata model describes the most elementary aspects of a harvested entity. A mapping ontology model has been prototyped in SMESE V1 to identify specific text segments based on thesauri in order to enrich content metadata with topics and emotions; this mapping ontology also allows interoperability between existing metadata models. Contribution 2: Metadata enrichments ecosystem based on topics and interests The second contribution extends the original SMESE V1 proposed in Contribution 1. Contribution 2 proposes a set of topic- and interest-based content semantic enrichments. The improved prototype, SMESE V3 (see following figure), uses text analysis approaches for sentiment and emotion detection and provides machine learning models to create a semantically enriched repository, thus enabling topic- and interest-based search and discovery. SMESE V3 has been designed to find short descriptions in terms of topics, sentiments and emotions. It allows efficient processing of large collections while keeping the semantic and statistical relationships that are useful for tasks such as: 1. topic detection, 2. contents classification, 3. novelty detection, 4. text summarization, 5. similarity detection. Contribution 3: Metadata-based scientific assisted literature review The third contribution proposes an assisted literature review (ALR) prototype, STELLAR V1 (Semantic Topics Ecosystem Learning-based Literature Assisted Review), based on machine learning models and a semantic metadata ecosystem. Its purpose is to identify, rank and recommend relevant papers for a literature review (LR). This third prototype can assist researchers, in an iterative process, in finding, evaluating and annotating relevant papers harvested from different sources and input into the SMESE V3 platform, available at any time. The key elements and concepts of this prototype are: 1. text and data mining, 2. machine learning models, 3. classification models, 4. researchers annotations, 5. semantically enriched metadata. STELLAR V1 helps the researcher to build a list of relevant papers according to a selection of metadata related to the subject of the ALR. The following figure presents the model, the related machine learning models and the metadata ecosystem used to assist the researcher in the task of producing an ALR on a specific topic

Developing an Agile Supply Chain Model for SMEs

Rising worldwide competition is making it increasingly difficult for SME organisations to compete in the marketplace as traditional means of manufacture, and modes of delivery are being changed through technological advancements. In line with these factors, organisations are ever more capable of producing goods that are more bespoke and personalised than in the past and within the price ranges and affordability levels of demanding markets. Whilst large organisations have the power to enforce supply chain compliance in order to meet these changes, it is not always the case for SMEs. The agile supply chain philosophy moves away from traditional methods under which large organisations enforce supply chain compliance, and embraces the concept of supply chain agility that allows the supply chain as a whole to move forward as one and share the benefits as a developed and cohesive unit. Such a philosophy should be to the advantage of all organisations, but ought to be of particular interest to SMEs as its use could assist in improving their competitiveness. This thesis is primarily concerned with the development of agile supply chains within SME organisations. The research sets out to develop the means through which SMEs can develop their agile supply chains so as to make them more efficient and competitive both now and in the future. The research is set upon existing theories and models, particularly following the works of Sharifi et al. (2006), Ismail and Sharifi (2006), Ismail et al., (2006) and Ismail et al., (2011) so as to contribute further to their concepts theoretically and to also present the practical means by which such frameworks can be utilised in industry. The research provides a link between manager perceptions and underlying factors that affect their organisations and how they relate to the markets served. This has been achieved through the development of a model through which SMEs can analyse their present operating position, consider new product features, potential supply chain partners and the means through which to develop their agile supply chains as a complete unit. Using case study methodology, some extensive fieldwork has been undertaken to examine the ideas and extend our understanding of the approaches to build and sustain agile networks for organisations introducing products into markets. The study has assisted in reforming and developing the initial models into practical tools. Further to this, the research offers a series of developmental roadmaps that can be followed by SMEs to assist in the progress of developing agility into their supply chains. The outcomes from the research provide a contribution to academic theory and practice and build upon previous research, taking it forward with practical tools that organisations can utilise. The findings provide evidence for the benefits that can be derived from the developed models such that their application could be realistically considered within a practical setting