A text-based approach to industry classification

Industry classification schemes are a critical topic in academic research due to their use in combining companies into smaller groups that share similar characteristics. Although many studies in the domains of economics, accounting and finance depend heavily on these schemes, existing ones have significant limitations mainly due to their stagnant nature, which makes the schemes incapable of adapting to constant innovation and technological development. The objective of this thesis is to propose an automated, text-based industry classification scheme that can reflect constant changes in industry scope. This thesis approaches the research problem by answering two research questions. First, it studies whether it is possible to build an industry classification scheme by using word-embedding vectors extracted from news article. Second, this thesis identifies the benefits of a text-based industry classification scheme in comparison with existing classification schemes. To identify benefits, both qualitative and quantitative assessments are conducted to measure the performance. In the construction of an industry classification scheme, word-embedding vectors generated from news articles are used. The vectors are built using the Word2Vec algorithm. Word2Vec is a recently developed text-mining tool and is excellent at capturing the relationships between words and expressing them in a quantifiable format. The key findings of this thesis are twofold. First, it is technically possible to build an automated, text-based industry classification scheme by using word-embedding vectors. Two methods of building the scheme are proposed. Second, the proposed text-based scheme performs well in classifying companies into relevant business categories. In addition, the cluster-based scheme exhibits better performance in grouping companies into financially homogenous groups when parameters are optimized. The results suggest that a text-based industry classification scheme can serve as an alternative to existing industry classification schemes if parameters are optimized to the purpose of its use. The usefulness of the scheme is expected to increase due to the accelerating speed of innovation and technological development

Agglomeration Economies and Heterogeneity within Young Innovative Companies

This paper fits into a new trend in empirical studies on agglomeration economies paying explicit attention to heterogeneity within innovative companies. The paper represents micro-level research, and is based on 21 in-depth case studies in a selected sample of young, innovative companies in the Netherlands. The selection criteria for sampling are derived from resource-based theory, e.g. age, size, corporate position, engaged in services or manufacturing industry. The selected sectors include mechatronics, biotechnology, ICT services and engineering services. In an attempt to identify causal factors and to identify different clusters of companies, we make use of rough set analysis, a method that typically fits small samples and qualitative data. Our research focuses on the importance perceived by company managers of a range of agglomeration advantages for the functioning of the company and on the perceived space in which the company could function satisfactorily. Based on our empirical explorations and given the theoretical positions of the selected case-studies, we arrive at the following findings (1) there is a divide of young, innovative companies into two, namely those facing a high level of importance (in large cities), and those facing a limited importance. In addition, network-based companies that outsource most of their activities to other companies may be facing no importance at all, potentially representing a third category; (2) the strongest factor influencing importance of agglomeration economies is corporate position, e.g. being a corporate spin-off or subsidiary (or not); (3) the spatial influence of agglomeration advantages tends to be broader than large cities only, but there are differences between the individual advantages, e.g. those working in a larger area of central cities, suburban places and medium-sized cities at larger distances, and those exclusively working in large cities or the largest city. Examples of the latter are a pool of young, internationally oriented labour force and direct access to the most advanced telecommunication infrastructure and services. The paper discusses the research design and the empirical outcomes and proposes various new hypotheses to be tested in large scale research.

Sentiment Analysis and Opinion Mining within Social Networks using Konstanz Information Miner

Evaluations, opinions, and sentiments have become very obvious due to rapid emerging interest in ecommerce which is also a significant source of expression of opinions and analysis of sentiment. In this study, a general introduction on sentiment analysis, steps of sentiment analysis, sentiments analysis applications, sentiment analysis research challenges, techniques used for sentiment analysis, etc., were discussed in detail. With these details given, it is hoped that researchers will engage in opinion mining and sentiment analysis research to attain more successes correlated to these issues. The research is based on data input from web services and social networks, including an application that performs such actions. The main aspects of this study are to statistically test and evaluate the major social network websites: In this case Twitter, because it is has rich data source and easy within social networks tools. In this study, firstly a good understanding of sentiment analysis and opinion mining research based on recent trends in the field is provided. Secondly, various aspects of sentiment analysis are explained. Thirdly, various steps of sentiment analysis are introduced. Fourthly, various sentiment analysis, research challenges are discussed. Finally, various techniques used for sentiment analysis are explained and Konstanz Information Miner (KNIME) that can be used as sentiment analysis tool is introduced. For future work, recent machine learning techniques including big data platforms may be proposed for efficient solutions for opinion mining and sentiment analysi

Detection of Drug Interactions via Android Smartphone: Design and Implementation

Despite the morbidity and cases of widespread drug poisoning, clinical guidelines are largely written by taking into account only one treatment at a time. The cumulative impact of multiple treatments is rarely considered. Drug treatment for people with several diseases produces a complex regimen called “polypharmacy” with a potential combination of harmful and even lethal drugs that can be prevented. This polypharmacy causes in many cases the death of some people due to drug interactions. The vast majority of these deaths can be prevented by detecting interactions before taking these medications. But the problem is that such information exists in a state that is difficult to access for the general public, much less for people with little knowledge in the field. Although the pharmacist is unmistakable and most viable source to avoid such a problem, he cannot know what the patient does not mention because he is not aware of what may affect his treatment. To remedy this, we aim in this paper to develop an ergonomic Android application that will inform the patient about the potential risks of such drug interactions. The application is optimized to handle various databases and operate automation of QR code

Explainability through transparency and user control: a case-based recommender for engineering workers.

Within the service providing industries, field engineers can struggle to access tasks which are suited to their individual skills and experience. There is potential for a recommender system to improve access to information while being on site. However the smooth adoption of such a system is superseded by a challenge for exposing the human understandable proof of the machine reasoning.With that in mind, this paper introduces an explainable recommender system to facilitate transparent retrieval of task information for field engineers in the context of service delivery. The presented software adheres to the five goals of an explainable intelligent system and incorporates elements of both Case-Based Reasoning and heuristic techniques to develop a recommendation ranking of tasks. In addition we evaluate methods of building justifiable representations for similarity-based return on a classification task developed from engineers' notes. Our conclusion highlights the trade-off between performance and explainability

Startups’ roads to failure

The role of a relatively small cadre of high-tech startup firms in driving innovation and economic growth has been well known and amply celebrated in recent history. At the same time, it is well recognized that, while the overall contribution of startups is crucial, the high-risk and high-reward strategy followed by these startups leads to significant failure rates and a low ratio of successful startups. So, it is curious to notice that literature tends to focus on successful startups and on quantitative studies looking for determinants of success while neglecting the numerous lessons that can be drawn by examining the stories of startups that failed. This paper aims to fill this gap and to contribute to the literature by providing a repeatable and scalable methodology that can be applied to databases of unstructured post-mortem documents deriving startup failure patterns. A further and related contribution is the analysis carried out with this methodology to a large database of 214 startup post-mortem reports. Descriptive statistics show how the lack of a structured Business Development strategy emerges as a key determinant of startup failure in the majority of cases

Text mining analysis roadmap (TMAR) for service research

Purpose The purpose of this paper is to offer a step-by-step text mining analysis roadmap (TMAR) for service researchers. The paper provides guidance on how to choose between alternative tools, using illustrative examples from a range of business contexts. Design/methodology/approach The authors provide a six-stage TMAR on how to use text mining methods in practice. At each stage, the authors provide a guiding question, articulate the aim, identify a range of methods and demonstrate how machine learning and linguistic techniques can be used in practice with illustrative examples drawn from business, from an array of data types, services and contexts. Findings At each of the six stages, this paper demonstrates useful insights that result from the text mining techniques to provide an in-depth understanding of the phenomenon and actionable insights for research and practice. Originality/value There is little research to guide scholars and practitioners on how to gain insights from the extensive “big data” that arises from the different data sources. In a first, this paper addresses this important gap highlighting the advantages of using text mining to gain useful insights for theory testing and practice in different service contexts. </jats:sec

Semantics-based clustering approach for similar research area detection

The manual process of searching out individuals in an already existing research field is cumbersome and time-consuming. Prominent and rookie researchers alike are predisposed to seek existing research publications in a research field of interest before coming up with a thesis. From extant literature, automated similar research area detection systems have been developed to solve this problem. However, most of them use keyword-matching techniques, which do not sufficiently capture the implicit semantics of keywords thereby leaving out some research articles. In this study, we propose the use of Ontology-based pre-processing, Latent Semantic Indexing and K-Means Clustering to develop a prototype similar research area detection system, that can be used to determine similar research domain publications. Our proposed system solves the challenge of high dimensionality and data sparsity faced by the traditional document clustering technique. Our system is evaluated with randomly selected publications from faculties in Nigerian universities and results show that the integration of ontologies in preprocessing provides more accurate clustering results

New techniques and framework for sentiment analysis and tuning of CRM structure in the context of Arabic language

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyKnowing customers’ opinions regarding services received has always been important for businesses. It has been acknowledged that both Customer Experience Management (CEM) and Customer Relationship Management (CRM) can help companies take informed decisions to improve their performance in the decision-making process. However, real-word applications are not so straightforward. A company may face hard decisions over the differences between the opinions predicted by CRM and actual opinions collected in CEM via social media platforms. Until recently, how to integrate the unstructured feedback from CEM directly into CRM, especially for the Arabic language, was still an open question. Furthermore, an accurate labelling of unstructured feedback is essential for the quality of CEM. Finally, CRM needs to be tuned and revised based on the feedback from social media to realise its full potential. However, the tuning mechanism for CEM of different levels has not yet been clarified. Facing these challenges, in this thesis, key techniques and a framework are presented to integrate Arabic sentiment analysis into CRM. First, as text pre-processing and classification are considered crucial to sentiment classification, an investigation is carried out to find the optimal techniques for the pre-processing and classification of Arabic sentiment analysis. Recommendations for using sentiment analysis classification in MSA as well as Saudi dialects are proposed. Second, to deal with the complexities of the Arabic language and to help operators identify possible conflicts in their original labelling, this study proposes techniques to improve the labelling process of Arabic sentiment analysis with the introduction of neural classes and relabelling. Finally, a framework for adjusting CRM via CEM for both the structure of the CRM system (on the sentence level) and the inaccuracy of the criteria or weights employed in the CRM system (on the aspect level) are proposed. To ensure the robustness and the repeatability of the proposed techniques and framework, the results of the study are further validated with real-word applications from different domains

A Bibliometric Analysis on Recent Classification Techniques for Alzheimer’s Disease

Alzheimer\u27s disease (AD) has been studied extensively to better understand the complexities of this disease and to address the numerous unanswered questions about prognosis and diagnosis. To be able to determine and allocate the resources appropriate to the research area, a detailed understanding of the research topic is much needed. Along with the tremendous expansion in the scope of neurodegenerative disease treatment research, the diversity of technologies to help the research continues to expand. Many studies have investigated into how AD affects different brain structures as the disease progresses, using various image processing methods to derive a variety of brain structure steps. To detect AD, structural magnetic resonance imaging (sMRI) is utilized to detect delicate structural variations in the brain. MRI is preferred over other modalities for identifying the structural changes in the brain caused by neurodegenerative diseases and their significance for AD diagnosis and prognosis. Hippocampal atrophy is a significant biomarker for assessing and diagnosing AD. The statistical properties obtained by texture analysis on the MRI based on a biomarker can be used to identify and further evaluate subtle changes in neurodegeneration. To distinguish normal control subjects from AD patients, various Neural Network-based algorithms have been developed. Consequently, this analysis focuses on understanding the recent developments by using an enriched collection of papers available on Scopus, and thus assists in understanding and providing a guided perspective for assigning research resources. The analysis is focusing on various statistical data obtained from Scopus, such as source, document type, affiliations, and so on, to analyze and collate current trends, research activity, and the impact of several notable writers, institutes/organizations, and countries in the respective research domain