Application of Statistical and Mathematical Algorithms to Data Analytics and Job Creation in Nigeria

In this paper, we examine the use of statistical and mathematical algorithms in data analytics and their application in business intelligence, insights and collective intelligence, for enhanced job creation interventions in Nigeria. The paper argues that the demand-driven job creation, involving developing skills for existing vacancies or opportunities is no longer sustainable in the current challenging economic conditions. Rather it makes a case for supply-driven job creation, where skills are developed in technology and data analytics (with strong reliance on statistics and mathematics), with a view to solving business and corporate problems, thereby enhancing job creation in those businesses and corporations, which hitherto had no vacancies. The paper surveys statistical and mathematical algorithms, categorized as supervised and unsupervised learning techniques, applied in data analytics, and discusses the emerging requirements for data analytics in modern business and corporations. It further discusses modern application of data analytics in a number of business areas such as marketing, customer management, finances, data mining, web and learning, highlighting a number of metrics specific to each sector. The paper also identifies the specialized skills required to create job opportunities in key sectors in Nigeria. Drawing extensively from the lead author’s experience in the UK, the paper presents how skills in modern data analytics can lead in creating job opportunities, a major lesson for Nigeria. Keywords: Job Creation, Data Analytics, Data Science, Business intelligence, Insights, Algorithm

THE IDENTIFICATION OF NOTEWORTHY HOTEL REVIEWS FOR HOTEL MANAGEMENT

The rapid emergence of user-generated content (UGC) inspires knowledge sharing among Internet users. A good example is the well-known travel site TripAdvisor.com, which enables users to share their experiences and express their opinions on attractions, accommodations, restaurants, etc. The UGC about travel provide precious information to the users as well as staff in travel industry. In particular, how to identify reviews that are noteworthy for hotel management is critical to the success of hotels in the competitive travel industry. We have employed two hotel managers to conduct an examination on Taiwan’s hotel reviews in Tripadvisor.com and found that noteworthy reviews can be characterized by their content features, sentiments, and review qualities. Through the experiments using tripadvisor.com data, we find that all three types of features are important in identifying noteworthy hotel reviews. Specifically, content features are shown to have the most impact, followed by sentiments and review qualities. With respect to the various methods for representing content features, LDA method achieves comparable performance to TF-IDF method with higher recall and much fewer features

Information Systems for “Wicked Problems” - Research at the Intersection of Social Media and Collective Intelligence

The objective of this commentary is to propose fruitful research directions built upon the reciprocal interplay of social media and collective intelligence. We focus on “wicked problems” – a class of problems that Introne et al. (Künstl. Intell. 27:45–52, 2013) call “prob- lems for which no single computational formulation of the problem is suffi- cient, for which different stakeholders do not even agree on what the prob- lem really is, and for which there are no right or wrong answers, only answers that are better or worse from differ- ent points of view”. We argue that in- formation systems research in partic- ular can aid in designing appropriate systems due to benefits derived from the combined perspectives of both so- cial media and collective intelligence. We document the relevance and time- liness of social media and collective in- telligence for business and information systems engineering, pinpoint needed functionality of information systems for wicked problems, describe related re- search challenges, highlight prospec- tive suitable methods to tackle those challenges, and review examples of initial results

Designing Human-Centered Collective Intelligence

Human-Centered Collective Intelligence (HCCI) is an emergent research area that seeks to bring together major research areas like machine learning, statistical modeling, information retrieval, market research, and software engineering to address challenges pertaining to deriving intelligent insights and solutions through the collaboration of several intelligent sensors, devices and data sources. An archetypal contextual CI scenario might be concerned with deriving affect-driven intelligence through multimodal emotion detection sources in a bid to determine the likability of one movie trailer over another. On the other hand, the key tenets to designing robust and evolutionary software and infrastructure architecture models to address cross-cutting quality concerns is of keen interest in the “Cloud” age of today. Some of the key quality concerns of interest in CI scenarios span the gamut of security and privacy, scalability, performance, fault-tolerance, and reliability. I present recent advances in CI system design with a focus on highlighting optimal solutions for the aforementioned cross-cutting concerns. I also describe a number of design challenges and a framework that I have determined to be critical to designing CI systems. With inspiration from machine learning, computational advertising, ubiquitous computing, and sociable robotics, this literature incorporates theories and concepts from various viewpoints to empower the collective intelligence engine, ZOEI, to discover affective state and emotional intent across multiple mediums. The discerned affective state is used in recommender systems among others to support content personalization. I dive into the design of optimal architectures that allow humans and intelligent systems to work collectively to solve complex problems. I present an evaluation of various studies that leverage the ZOEI framework to design collective intelligence

Mining social media data for biomedical signals and health-related behavior

Social media data has been increasingly used to study biomedical and health-related phenomena. From cohort level discussions of a condition to planetary level analyses of sentiment, social media has provided scientists with unprecedented amounts of data to study human behavior and response associated with a variety of health conditions and medical treatments. Here we review recent work in mining social media for biomedical, epidemiological, and social phenomena information relevant to the multilevel complexity of human health. We pay particular attention to topics where social media data analysis has shown the most progress, including pharmacovigilance, sentiment analysis especially for mental health, and other areas. We also discuss a variety of innovative uses of social media data for health-related applications and important limitations in social media data access and use.Comment: To appear in the Annual Review of Biomedical Data Scienc

Predictive Analysis on Twitter: Techniques and Applications

Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques, approaches and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking actions, and relate a few success stories

Assessment, Implication, and Analysis of Online Consumer Reviews: A Literature Review

The onset of e-marketplace, virtual communities and social networking has appreciated the influential capability of online consumer reviews (OCR) and therefore necessitate conglomeration of the body of knowledge. This article attempts to conceptually cluster academic literature in both management and technical domain. The study follows a framework which broadly clusters management research under two heads: OCR Assessment and OCR Implication (business implication). Parallel technical literature has been reviewed to reconcile methodologies adopted in the analysis of text content on the web, majorly reviews. Text mining through automated tools, algorithmic contribution (dominant majorly in technical stream literature) and manual assessment (derived from the stream of content analysis) has been studied in this review article. Literature survey of both the domains is analyzed to propose possible area for further research. Usage of text analysis methods along with statistical and data mining techniques to analyze review text and utilize the knowledge creation for solving managerial issues can possibly constitute further work. Available at: https://aisel.aisnet.org/pajais/vol9/iss2/4

Information Systems for “Wicked Problems” – Proposing Research at the Intersection of Social Media and Collective Intelligence

The objective of this commentary is to propose some fruitful research direction built upon the reciprocal interplay of social media and collective intelligence. We focus on “wicked problems” — a class of what Introne et al. 2013 call “problems for which no single computational formulation of the problem is sufficient, for which different stakeholders do not even agree on what the problem really is, and for which there are no right or wrong answers, only answers that are better or worse from different points of view”. We argue that information systems research in particular can aid in designing appropriate systems due to benefits derived from the combined perspectives of both social media and collective intelligence. We document the relevance and timeliness of social media and collective intelligence for business and information systems engineering, pinpoint needed functionality of information systems for wicked problems, describe related research challenges, highlight prospective suitable methods to tackle those challenges, and review examples of initial results