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

Social Data Mining for Crime Intelligence

With the advancement of the Internet and related technologies, many traditional crimes have made the leap to digital environments. The successes of data mining in a wide variety of disciplines have given birth to crime analysis. Traditional crime analysis is mainly focused on understanding crime patterns, however, it is unsuitable for identifying and monitoring emerging crimes. The true nature of crime remains buried in unstructured content that represents the hidden story behind the data. User feedback leaves valuable traces that can be utilised to measure the quality of various aspects of products or services and can also be used to detect, infer, or predict crimes. Like any application of data mining, the data must be of a high quality standard in order to avoid erroneous conclusions. This thesis presents a methodology and practical experiments towards discovering whether (i) user feedback can be harnessed and processed for crime intelligence, (ii) criminal associations, structures, and roles can be inferred among entities involved in a crime, and (iii) methods and standards can be developed for measuring, predicting, and comparing the quality level of social data instances and samples. It contributes to the theory, design and development of a novel framework for crime intelligence and algorithm for the estimation of social data quality by innovatively adapting the methods of monitoring water contaminants. Several experiments were conducted and the results obtained revealed the significance of this study in mining social data for crime intelligence and in developing social data quality filters and decision support systems

Electronic word-of-mouth in online brand communities: drivers and outcomes

Current study advances the understanding of electronic word-of-mouth (eWOM) in the context of online brand communities (OBC) embedded in social media. The focal concept of this thesis is OBCeWOM, which represents a behavioural manifestation of OBC engagement – a growing stream of research in the brand community literature. By connecting the two key streams of research on online consumer-to-consumer and consumer-brand interactions, the current thesis addresses the nature, drivers and outcomes of OBCeWOM in the social media setting. The study follows a sequential mixed-methods research design, where the data was first collected via 22 semi-structured interviews, followed by a survey of 652 members of social media-based OBCs. The research was divided into three studies in line with the stated research questions. Consistent with the RQ2 and RQ3, Study 1 utilised semi-structured interviews to identify the key motivations for and outcomes of OBCeWOM in the social media setting which were consequently included in the finalised conceptual framework. Following this, Study 2 relied on interview and survey data to answer the RQ1 by clarifying the dimensionality of and developing a new measurement scale for OBCeWOM. Finally, Study 3 utilised the survey data to confirm the relationships hypothesised in the conceptual model and answer the RQ2 and RQ3. Findings of this thesis confirm the multi-dimensional nature of OBCeWOM, consisting of reading, posting and sharing components and offer a new reliable measurement for eWOM in the OBC context. Results of the study further identify four key motivations of OBCeWOM in the social media setting, including getting assistance from the brand, social interaction, social expression of opinions and expressing positive emotions. Concurrently, self-expression motivation has a negative effect on OBCeWOM. Finally, this thesis confirms the role of OBCeWOM in brand trust, brand loyalty, and oppositional brand loyalty. Current research offers several theoretical, methodological and managerial implications