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Advanced applications of NLP and deep learning in social media data
\"The primary objective of this book is to build a better and safer social media space by making human language available on different social media platforms intelligible for machines with the blessings of AI. This book bridges the gap between Natural Language Processing (NLP), Advanced Machine(AML) and Deep Learning (DL), and Online Social Media. This book connects various interdisciplinary domains related to Natural Language Understanding, Deep machine Leaning Technology and will be highly beneficial for the students, researchers, and academicians working in this area as this book will cover state-of-the-art technologies around NLP and DML techniques and their role in Social Media Data Analysis. Furthermore, the OSN service providers will take the advantage of this book to update, modify and make better social platforms for its users. Psychiatrists and clinicians will also be beneficial as this book's main focus are to analyze the user behavior in Online Social networks which play a key ingredient in several psychological tests\"-- Provided by publisher.
Book
Vector-Space Models of Semantic Representation From a Cognitive Perspective
Models that represent meaning as high-dimensional numerical vectors—such as latent semantic analysis (LSA), hyperspace analogue to language (HAL), bound encoding of the aggregate language environment (BEAGLE), topic models, global vectors (GloVe), and word2vec—have been introduced as extremely powerful machine-learning proxies for human semantic representations and have seen an explosive rise in popularity over the past 2 decades. However, despite their considerable advancements and spread in the cognitive sciences, one can observe problems associated with the adequate presentation and understanding of some of their features. Indeed, when these models are examined from a cognitive perspective, a number of unfounded arguments tend to appear in the psychological literature. In this article, we review the most common of these arguments and discuss (a) what exactly these models represent at the implementational level and their plausibility as a cognitive theory, (b) how they deal with various aspects of meaning such as polysemy or compositionality, and (c) how they relate to the debate on embodied and grounded cognition. We identify common misconceptions that arise as a result of incomplete descriptions, outdated arguments, and unclear distinctions between theory and implementation of the models. We clarify and amend these points to provide a theoretical basis for future research and discussions on vector models of semantic representation.
Journal Article
Memorization and the compound-phrase distinction : an investigation of complex constructions in German, French and English
\"Over the last decades, it has been hotly debated whether and how compounds, i.e. word-formations, and phrases differ from each other. The book discusses this issue by investigating compounds and phrases from a structural, semantic-functional and, crucially, cognitive perspective. The analysis focuses on compounds and phrases that are composed of either an adjective and a noun or two nouns in German, French and English. Having distinguished compounds from phrases on structural and semantic-functional grounds, the author claims that compounds are by their nature more appropriate to be stored in the mental lexicon than phrases and supports his argument with empirical evidence from new psycholinguistic studies. In sum, the book maintains the separation between compounds and phrases and reflects upon its cognitive consequences\"-- Provided by publisher.
Book
Topic Modeling: A Comprehensive Review
Topic modelling is the new revolution in text mining. It is a statistical technique for revealing the underlying semantic structure in large collection of documents. After analysing approximately 300 research articles on topic modeling, a comprehensive survey on topic modelling has been presented in this paper. It includes classification hierarchy, Topic modelling methods, Posterior Inference techniques, different evolution models of latent Dirichlet allocation (LDA) and its applications in different areas of technology including Scientific Literature, Bioinformatics, Software Engineering and analysing social network is presented. Quantitative evaluation of topic modeling techniques is also presented in detail for better understanding the concept of topic modeling. At the end paper is concluded with detailed discussion on challenges of topic modelling, which will definitely give researchers an insight for good research.
Journal Article
The roles of associative and executive processes in creative cognition
How does the mind produce creative ideas? Past research has pointed to important roles of both executive and associative processes in creative cognition. But such work has largely focused on the influence of one ability or the other—executive or associative—so the extent to which both abilities may jointly affect creative thought remains unclear. Using multivariate structural equation modeling, we conducted two studies to determine the relative influences of executive and associative processes in domain-general creative cognition (i.e., divergent thinking). Participants completed a series of verbal fluency tasks, and their responses were analyzed by means of latent semantic analysis (LSA) and scored for semantic distance as a measure of associative ability. Participants also completed several measures of executive function—including broad retrieval ability (Gr) and fluid intelligence (Gf). Across both studies, we found substantial effects of both associative and executive abilities: As the average semantic distance between verbal fluency responses and cues increased, so did the creative quality of divergent-thinking responses (Study
1
and Study
2
). Moreover, the creative quality of divergent-thinking responses was predicted by the executive variables—Gr (Study
1
) and Gf (Study
2
). Importantly, the effects of semantic distance and the executive function variables remained robust in the same structural equation model predicting divergent thinking, suggesting unique contributions of both constructs. The present research extends recent applications of LSA in creativity research and provides support for the notion that both associative and executive processes underlie the production of novel ideas.
Journal Article
A Tool for Addressing Construct Identity in Literature Reviews and Meta-Analyses
The problem of detecting whether two behavioral constructs reference the same real-world phenomenon has existed for over 100 years. Discordant naming of constructs is here termed the construct identity fallacy (CIF). We designed and evaluated the construct identity detector (CID), the first tool with large-scale construct identity detection properties and the first tool that does not require respondent data. Through the adaptation and combination of different natural language processing (NLP) algorithms, six designs were created and evaluated against human expert decisions. All six designs were found capable of detecting construct identity, and a design combining two existing algorithms significantly outperformed the other approaches. A set of follow-up studies suggests the tool is valuable as a supplement to expert efforts in literature review and metaanalysis. Beyond design science contributions, this article has important implications related to the taxonomic structure of social and behavioral science constructs, for the jingle and jangle fallacy, the core of the Information Systems nomological network, and the inaccessibility of social and behavioral science knowledge. In sum, CID represents an important, albeit tentative, step toward discipline-wide identification of construct identities.
Journal Article
Recent trends of green human resource management: Text mining and network analysis
Issues of the environmental crisis are being addressed by researchers, government, and organizations alike. GHRM is one such field that is receiving lots of research focus since it is targeted at greening the firms and making them eco-friendly. This research reviews 317 articles from the Scopus database published on green human resource management (GHRM) from 2008 to 2021. The study applies text mining, latent semantic analysis (LSA), and network analysis to explore the trends in the research field in GHRM and establish the relationship between the quantitative and qualitative literature of GHRM. The study has been carried out using KNIME and VOSviewer tools. As a result, the research identifies five recent research trends in GHRM using K-mean clustering. Future researchers can work upon these identified trends to solve environmental issues, make the environment eco-friendly, and motivate firms to implement GHRM in their practices.
Journal Article
Analysis and synthesis of Industry 4.0 research landscape
PurposeIn recent years, Industry 4.0 has received immense attention from academic community, practitioners and the governments across nations resulting in explosive growth in the publication of articles, thereby making it imperative to reveal and discern the core research areas and research themes of Industry 4.0 extant literature. The purpose of this paper is to discuss research dynamics and to propose a taxonomy of Industry 4.0 research landscape along with future research directions.Design/methodology/approachA data-driven text mining approach, Latent Semantic Analysis (LSA), is used to review and extract knowledge from the large corpus of the 503 abstracts of academic papers published in various journals and conference proceedings. The adopted technique extracts several latent factors that characterise the emerging pattern of research. The cross-loading analysis of high-loaded papers is performed to identify the semantic link between research areas and themes.FindingsLSA results uncover 13 principal research areas and 100 research themes. The study discovers “smart factory” and “new business model” as dominant research areas. A taxonomy is developed which contains five topical areas of Industry 4.0 field.Research limitations/implicationsThe data set developed is based on systematic article refining process which includes the keywords search in selected electronic databases and articles limited to English language only. So, there is a possibility that other related work may not be captured in the data set which may be published in other than examined databases and are in non-English language.Originality/valueTo the best of the authors’ knowledge, this study is the first of its kind that has used the LSA technique to reveal research trends in Industry 4.0 domain. This review will be beneficial to scholars and practitioners to understand the diversity and to draw a roadmap of Industry 4.0 research. The taxonomy and outlined future research agenda could help the practitioners and academicians to position their research work.
Journal Article
A semantic network analysis of categorization in open government data portals
Purpose
This study aims to evaluate the semantic relationships between category terms that are used in open government data (OGD) portals and those identified in policy documents through the implementation of a semantic network analysis.
Design/methodology/approach
This study was conducted in three stages. Firstly, the study examined the semantic relationships between category terms in OGD portals by constructing a similarity matrix based on the terms’ co-occurrence and visualizing six-word groups. Secondly, the study investigated the semantic relationships among terms in OGD policy documents using latent semantic analysis and community detection methods, resulting in the identification and visualization of three network groups. Finally, the study used chi-squared and Z-tests to analyse differences in category terms between countries with and without redefined categories.
Findings
The results indicate that the three-word groups were identified by community detection, covering various aspects of government. In addition, there is a significant difference between the two country groups, with category terms being more prevalent in countries with predefined categories. This emphasizes the impact of categorization on term prevalence within OGD portals.
Originality/value
This study uniquely focuses on the categorization of government portals for sustainable open data management. The findings underscore the importance of effectively structuring and organizing data categories to enhance user discoverability and accessibility in OGD portals.
Journal Article
Quantitative approaches to content analysis: identifying conceptual drift across publication outlets
Unstructured text data, such as emails, blogs, contracts, academic publications, organizational documents, transcribed interviews, and even tweets, are important sources of data in Information Systems research. Various forms of qualitative analysis of the content of these data exist and have revealed important insights. Yet, to date, these analyses have been hampered by limitations of human coding of large data sets, and by bias due to human interpretation. In this paper, we compare and combine two quantitative analysis techniques to demonstrate the capabilities of computational analysis for content analysis of unstructured text. Specifically, we seek to demonstrate how two quantitative analytic methods, viz., Latent Semantic Analysis and data mining, can aid researchers in revealing core content topic areas in large (or small) data sets, and in visualizing how these concepts evolve, migrate, converge or diverge over time. We exemplify the complementary application of these techniques through an examination of a 25-year sample of abstracts from selected journals in Information Systems, Management, and Accounting disciplines. Through this work, we explore the capabilities of two computational techniques, and show how these techniques can be used to gather insights from a large corpus of unstructured text.
Journal Article