A semi-auto text mining approach for literature review

The emergence of powerful information technologies (IT) has changed innovation and entrepreneurship in significant ways. Research in IT for entrepreneurship is relatively new and there is a growing interest from academics for further studies on investigating this area of research. This study reviews research carried out in the domain of IT for entrepreneurship. A total of 1005 papers, published between 1980 and 2021 were used to uncover the latent topics addressed in this domain. A topic modeling (LDA) algorithm was used to automate the process of extracting the initial research topics from the data. The results show that IT for entrepreneurship studies are classified into six aspects of research: entrepreneurship initiative and innovation, strategy, business process management and operation management, entrepreneurship education, industry analysis, and business model. The results raise awareness of IT-associated entrepreneurship areas of research, provide useful insights for future research, and informs practice in this domain of study.

Digital voice-of-customer processing by topic modelling algorithms: insights to validate empirical results

PurposeDigital voice-of-customer (digital VoC) analysis is gaining much attention in the field of quality management. Digital VoC can be a great source of knowledge about customer needs, habits and expectations. To this end, the most popular approach is based on the application of text mining algorithms named topic modelling. These algorithms can identify latent topics discussed within digital VoC and categorise each source (e.g. each review) based on its content. This paper aims to propose a structured procedure for validating the results produced by topic modelling algorithms.Design/methodology/approachThe proposed procedure compares, on random samples, the results produced by topic modelling algorithms with those generated by human evaluators. The use of specific metrics allows to make a comparison between the two approaches and to provide a preliminary empirical validation.FindingsThe proposed procedure can address users of topic modelling algorithms in validating the obtained results. An application case study related to some car-sharing services supports the description.Originality/valueDespite the vast success of topic modelling-based approaches, metrics and procedures to validate the obtained results are still lacking. This paper provides a first practical and structured validation procedure specifically employed for quality-related applications.

Gender distribution across topics in the top five economics journals: a machine learning approach

We analyze text data in all the articles published in the top five (T5) economics journals between 2002 and 2019 in order to find gender differences in their research approach. We implement an unsupervised machine learning algorithm: the structural topic model (STM), so as to incorporate gender document-level meta-data into a probabilistic text model. This algorithm characterizes jointly the set of latent topics that best fits our data (the set of abstracts) and how the documents/abstracts are allocated to each topic. Latent topics are mixtures over words where each word has a probability of belonging to a topic after controlling by journal name and publication year (the meta-data). Thus, the topics may capture research fields but also other more subtle characteristics related to the way in which the articles are written. We find that females are unevenly distributed over the estimated latent topics. This and other findings rely on “automatically” generated built-in data given the contents in the abstracts of the articles in the T5 journals, without any arbitrary allocation of texts to particular categories (as JEL codes, or research areas).

Modeling Latent Topics in Social Media using Dynamic Exploratory Graph Analysis: The Case of the Right-wing and Left-wing Trolls in the 2016 US Elections

The past few years were marked by increased online offensive strategies perpetrated by state and non-state actors to promote their political agenda, sow discord, and question the legitimacy of democratic institutions in the US and Western Europe. In 2016, the US congress identified a list of Russian state-sponsored Twitter accounts that were used to try to divide voters on a wide range of issues. Previous research used latent Dirichlet allocation (LDA) to estimate latent topics in data extracted from these accounts. However, LDA has characteristics that may limit the effectiveness of its use on data from social media: The number of latent topics must be specified by the user, interpretability of the topics can be difficult to achieve, and it does not model short-term temporal dynamics. In the current paper, we propose a new method to estimate latent topics in texts from social media termed Dynamic Exploratory Graph Analysis (DynEGA). In a Monte Carlo simulation, we compared the ability of DynEGA and LDA to estimate the number of simulated latent topics. The results show that DynEGA is substantially more accurate than several different LDA algorithms when estimating the number of simulated topics. In an applied example, we performed DynEGA on a large dataset with Twitter posts from state-sponsored right- and left-wing trolls during the 2016 US presidential election. DynEGA revealed topics that were pertinent to several consequential events in the election cycle, demonstrating the coordinated effort of trolls capitalizing on current events in the USA. This example demonstrates the potential power of our approach for revealing temporally relevant information from qualitative text data.

Topic Modeling of Committee Discussions in the Brazilian Chamber of Deputies

Ensuring that civil society can monitor and supervise the actions of its representatives is essential to build strong democracies. Despite significant advances in transparency, Brazilian National Congress committees are presently complex to follow and monitor due to the lack of open structured data about their discussions and the sheer volume of activity in these committees. This work presents two contributions to this context. First, we create and present an open dataset including structured speeches of the 25 Chamber of Deputies' standing committees over the last two decades. Second, we use Natural Language Processing techniques - especially Latent Dirichlet Allocation (LDA) - to identify themes addressed on these committees. Based on these latent topics, we explore similarities and differences between the standing committees, their relationships, and how their debates change over time. Our results show that committees accommodate conversations - including their main topic and opposing agendas - and describe how the topics discussed in the committees reverberate external events.

Evaluating latent content within unstructured text: an analytical methodology based on a temporal network of associated topics

In this research various concepts from network theory and topic modelling are combined, to provision a temporal network of associated topics. This solution is presented as a step-by-step process to facilitate the evaluation of latent topics from unstructured text, as well as the domain area that textual documents are sourced from. In addition to ensuring shifts and changes in the structural properties of a given corpus are visible, non-stationary classes of cooccurring topics are determined, and trends in topic prevalence, positioning, and association patterns are evaluated over time. The aforementioned capabilities extend the insights fostered from stand-alone topic modelling outputs, by ensuring latent topics are not only identified and summarized, but more systematically interpreted, analysed, and explained, in a transparent and reliable way.

What are the Latent Topics Associated with Experience in Flipped Learning? An Empirical Investigation of University Students' Interview Responses

With the advancement of ICT in the 21st century, ICT adopted education environment and teaching approaches are introduced in Higher Education setting globally. In the year of 2020 when global education sector encountered unprecedent difficult phenomena in Covid-19 Pandemic, teachers, students, and education administrators had to familiar with the terminology of online platform, hybrid learning and Flipped Learning (FL). With this strong intervention of ICT into the Education setting, this study aims to explore the students’ experience and perception toward the FL by analyzing and interpreting interview data from the qualitative studies on FL between 2014 and 2020. This study conducted text mining analysis, and topic modeling method from the selected 102 SSCI and Scopus level research articles on FL. The result of the study categorized the findings from the pure text analysis into three themes for the FL which were ‘teacher and classroom’, ‘motivation and students’ growth’, ‘educational needs and difficulties. From the result, the study confirmed the other two key topics which are related to student’ experience and characteristics such as growth of motivation, practical challenges, and difficulties from the FL experience. The result indicates that teachers need to give attentive attention and observation to the challenges that students are encountering during the classroom activities in FL setting.

Best Paper Selection

Fabregat A, Magret M, Ferré JA, Vernet A, Guasch N, Rodríguez A, Gómez J, Bodí M. A Machine Learning decision-making tool for extubation in Intensive Care Unit patients. https://www.sciencedirect.com/science/article/abs/pii/S0169260720317028?via%3Dihub Kempa-Liehr AW, Lin CYC, Britten R, Armstrong D, Wallace J, Mordaunt D, O’Sullivan M. Healthcare pathway discovery and probabilistic machine learning. https://www.sciencedirect.com/science/article/abs/pii/S1386505619308068?via%3Dihub Li Y, Nair P, Lu XH, Wen Z, Wang Y, Dehaghi AAK, Miao Y, Liu W, Ordog T, Biernacka JM, Ryu E, Olson JE, Frye MA, Liu A, Guo L, Marelli A, Ahuja Y, Davila-Velderrain J, Kellis M. Inferring multimodal latent topics from electronic health records. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7242436/ Weemaes M, Martens S, Cuypers L, van Elslande J, Hoet K, Welkenhuysen J, Goossens R, Wouters S, Houben E, Jeuris E, Jeuris K, Laenen L, Bruyninckx K, Beuselinck K, André E, Depypere M, Desmet S, Lagrou K, Van Ranst M, Verdonck AKLC, Goveia J. Laboratory information system requirements to manage the COVID-19 pandemic: A report from the Belgian national reference testing center. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7197526/

Infodemiology: Computational Methodologies for quantifying and visualizing key characteristics of the COVID-19 infodemic

Objectives. Infodemics of false information on social media is a growing societal problem, aggravated by the occurrence of the COVID-19 pandemic. The development of infodemics has characteristic resemblances to epidemics of infectious diseases. This paper presents several methodologies which aim to measure the extent and development of infodemics through the lens of epidemiology.Methods. Time varying R was used as a measure for the infectiousness of the infodemic, topic modeling was used to create topic clouds and topic similarity heat maps, while network analysis was used to create directed and undirected graphs to identify super-spreader and multiple carrier communities on social media.Results. Forty-two (42) latent topics were discovered. Reproductive trends for a specific topic were observed to have significantly higher peaks (Rt 4-5) than general misinformation (Rt 1-3). From a sample of social media misinformation posts, a total of 385 groups and 804 connections were found within the network, with the largest group having 1,643 shares and 1,063,579 interactions over a 12 month period.Conclusions. These approaches enable the measurement of the infectiousness of an infodemic, comparative analysis of infodemic topics, and identification of likely super-spreaders and multiple carriers on social media. The results of these analyses can form the basis for taking action to stem an ongoing spread of misinformation on social media and mitigate against future infodemics. The methods are not confined to health misinformation and may be applied to other infodemics, such as conspiracy theories, political disinformation, and climate change denial.

Understanding vaccine hesitancy with application of Latent Dirichlet Allocation to Reddit Corpora

This research paper explores the underlying factors that contribute toward vaccine hesitancy, resistance, and refusal. Using Latent Dirichlet Allocation (LDA), an unsupervised generative-probabilistic model, we generated latent topics from user generated Reddit corpora on reasons for Vaccine hesitancy. Although we hoped to explore the grounds for vaccine hesitancy across the globe, our findings suggest that the corpus used for analysis had been generated by users living predominantly in the United States.Observation of the topics generated by the LDA model led to the discovery of the following latent factors: (i) fear of risks and side effects, (ii) lack of trust in policymakers, (iii) related to religious belief, (iv) related to mass surveillance theories, (v) perception of vaccination as a precedence to totalitarianism, (vi) racial background pertaining to retrospective events of racial injustice, such as selective sterilization, (vii) depopulation agenda fueled by theories affiliated to Global warming and extinction rebellion, (viii) and perception of vaccination as a campaign to quell immigrant population growth, fueled by reports of coerced sterilization of immigrants in the ICE detention.