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The amount of textual data that is available for researchers and businesses to analyze is increasing at a dramatic rate. This reality has led IS researchers to investigate various text mining techniques. This essay examines four text mining methods that are frequently used in order to identify their characteristics and limitations. The four methods that we examine are (1) latent semantic analysis, (2) probabilistic latent semantic analysis, (3) latent Dirichlet allocation, and (4) correlated topic model. We review these four methods and compare them with topic detection and spam filtering to reveal their peculiarity. Our paper sheds light on the theory that underlies text mining methods and provides guidance for researchers who seek to apply these methods.

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 We identify common misconceptions that arise as a result of incomplete descriptions, outdated arguments, and unclear distinctions between theory and implementation of the models.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.

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.

Automatic Essay Assessor (AEA) is a system that utilizes information retrieval techniques such as Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Analysis (PLSA), and Latent Dirichlet Allocation (LDA) for automatic essay grading. The system uses learning materials and relatively few teacher-graded essays for calibrating the scoring mechanism before grading. We performed a series of experiments using LSA, PLSA and LDA for document comparisons in AEA. In addition to comparing the methods on a theoretical level, we compared the applicability of LSA, PLSA, and LDA to essay grading with empirical data. The results show that the use of learning materials as training data for the grading model outperforms the k-NN-based grading methods. In addition to this, we found that using LSA yielded slightly more accurate grading than PLSA and LDA. We also found that the division of the learning materials in the training data is crucial. It is better to divide learning materials into sentences than paragraphs.

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.

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.

This paper develops a protocol for using a familiar data set on force majeure provisions in corporate acquisitions agreements to tokenize and calibrate a machinelearning algorithm of textual analysis. Our protocol, built on regular expression (RE) and latent semantic analysis (LSA) approaches, serves to replicate, correct, and extend the hand-coded data. Our preliminary results indicate that both approaches perform well, though a hybridized approach improves predictive power further. Monte Carlo simulations suggest that our results are generally robust to out-of-sample predictions. We conclude that similar approaches could be used more broadly in empirical legal scholarship, especially including in business law.

[Display omitted] •Understanding the drivers of organic clicks for search engine optimization (SEO).•Develop a model that provides guidance for SEO practitioners on keyword selection.•Online authority is important at driving organic clicks for informational searches.•Content relevance is important at driving organic clicks for transactional searches. We build an empirical framework using search queries and organic click data which provides model-based guidance to SEO practitioners for keyword selection and web content creation. Specifically, we study how search characteristics (search query popularity, search query competition, search query specificity, and search intent) and website characteristics (content relevance and online authority) interact to affect the expected organic clicks as well as the organic rank a website receives from the search engine result page (SERP). It is often thought that content relevance is a key factor to improve the effectiveness of SEO. We find, however, that content relevance is an important factor in driving organic clicks only when the consumer is farther along in the customer journey and searching for ways to purchase a product. Whereas, when the customer is at the awareness stage and looking for product information, online authority is the key driver of organic clicks.

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.