Text Analytics Approach Research Articles

Star architecture in online public discourse: exploring Reddit user-generated content on the Vessel, New York, through a text analytics approach

Star architecture in online public discourse: exploring Reddit user-generated content on the Vessel, New York, through a text analytics approach

Songs tell a story: The Arc of narrative for music.

Research suggests that a core lexical structure characterized by words that define plot staging, plot progression, and cognitive tension underlies written narratives. Here, we investigate the extent to which song lyrics follow this underlying narrative structure. Using a text analytic approach and two publicly available datasets of song lyrics including a larger dataset (N = 12,280) and a smaller dataset of greatest hits (N = 2,823), we find that music lyrics tend to exhibit a core Arc of Narrative structure: setting the stage at the beginning, progressing the plot steadily until the end of the song, and peaking in cognitive tension in the middle. We also observe differences in narrative structure based on musical genre, suggesting different genres set the scene in greater detail (Country, Rap) or progress the plot faster and have a higher rate of internal conflict (Pop). These findings add to the evidence that storytelling exhibits predictable language patterns and that storytelling is evident in music lyrics.

Exploring over a decade of systems engineering research center: A community detection and text analytics approach

AbstractThe Systems Engineering Research Center (SERC) is a University Affiliated Research Center (UARC) of the US Department of Defense (DoD) formed in 2008 with more than 20 collaborator universities in the United States. Over the last decade, SERC has conducted research with Principal Investigators from universities within the SERC network, as reflected in technical reports (TR). These reports describe detailed information and analysis of the conducted research for every project under SERC support, such as written records of experiments or results of a scientific project. We analyzed the TRs from 2009 to early 2023 to identify research streams, topics, and evolution in systems engineering (SE) research using text mining and network analysis techniques, such as Louvain Community Detection and word similarity. As a result, we identified four major research streams over a decade of research projects, along with insights about topics and the evolution of SE within this time frame. Finally, we distinguished most profile authors and their most significant collaborations and networks.

Analyzing the Impact of Online Learning on Higher Education: A Text Analytics Approach

Amidst the relentless upheaval caused by the ongoing Covid-19 pandemic, the higher education landscape finds itself compelled to pivot towards internet-mediated learning modalities. This shift, while necessary for continuity, has engendered profound repercussions for students, educators, and administrative staff alike. Foremost among the concerns is the discernible impact on student learning outcomes and academic performance. Studies, such as those conducted by Brookings and The University of Chicago, underscore the alarming projections of learning loss and escalating failure rates within this context. Bloom, a prominent higher education institution grappling with the tumult of the pandemic, has witnessed a palpable decline in average grades since its onset. Recognizing the imperative to stem this tide and foster informed decision-making, Bloom endeavors to harness the power of text analytics. Through the systematic analysis of unstructured textual data sourced from diverse channels—ranging from social media platforms to educational websites—Bloom endeavors to unveil underlying patterns, discern actionable insights, and drive strategic interventions. This article presents a comprehensive framework delineating Bloom's foray into text analytics, elucidating the attendant challenges, proposed solutions, and anticipated implementation strategies. By delving into the nuances of managing unstructured textual data and navigating the complexities thereof, this endeavor seeks to empower Bloom with the tools and insights requisite for optimizing academic performance and mitigating the deleterious effects of the pandemic.

Tracking mosquito-borne diseases via social media: a machine learning approach to topic modelling and sentiment analysis

Mosquito-borne diseases (MBDs) are a major threat worldwide, and public consultation on these diseases is critical to disease control decision-making. However, traditional public surveys are time-consuming and labor-intensive and do not allow for timely decision-making. Recent studies have explored text analytic approaches to elicit public comments from social media for public health. Therefore, this study aims to demonstrate a text analytics pipeline to identify the MBD topics that were discussed on Twitter and significantly influenced public opinion. A total of 25,000 tweets were retrieved from Twitter, topics were modelled using LDA and sentiment polarities were calculated using the VADER model. After data cleaning, we obtained a total of 6,243 tweets, which we were able to process with the feature selection algorithms. Boruta was used as a feature selection algorithm to determine the importance of topics to public opinion. The result was validated using multinomial logistic regression (MLR) performance and expert judgement. Important issues such as breeding sites, mosquito control, impact/funding, time of year, other diseases with similar symptoms, mosquito-human interaction and biomarkers for diagnosis were identified by both LDA and experts. The MLR result shows that the topics selected by LASSO perform significantly better than the other algorithms, and the experts further justify the topics in the discussion.

A text analytics approach for mining public discussions in online cancer forum: Analysis of multi-intent lung cancer treatment dataset

BackgroundOnline cancer forums (OCF) are increasingly popular platforms for patients and caregivers to discuss, seek information on, and share opinions about diseases and treatments. This interaction generates a substantial amount of unstructured text data, necessitating deeper exploration. Using time series data, our study exploits topic modeling in the novel domain of online cancer forums (OCFs) to identify meaningful topics and changing dynamics of online discussion across different lung cancer treatment intent groups. MethodsFor this purpose, a dataset comprising 27,998 forum posts about lung cancer was collected from three OCFs: lungcancer.net, lungevity.org, and reddit.com, spanning the years 2016 to 2018. ResultsThe analysis reflects the public discussion on multi-intent lung cancer treatment over time, taking into account seasonal variations. Discussions on cancer symptoms and prevention garnered the most attention, dominating both curative and palliative care discussions. There were distinct seasonal peaks: curative care topics surged from winter to late spring, while palliative care topics peaked from late summer to mid-autumn. Keyword analysis highlighted that lung cancer diagnosis and treatment were primary topics, whereas cancer prevention and treatment outcomes were predominant across multi-care contexts. For the study period, curative care discussions predominantly revolved around informational support and disease syndromes. In contrast, social support and cancer prevention prevailed in the palliative care context. Notably, topics such as cancer screening and cancer treatment exhibit pronounced seasonal variations in curative care, peaking in frequency during the summers (May to August) of the study period. Meanwhile, the topic of tumor control within palliative care showed significant seasonal influence during the winters and summers of 2017 and 2018. ConclusionOur text analysis approach using OCF data shows potential for computational methods in this novel domain to gain insights into trends in public cancer communication and seasonal variations for a better understanding of improving personalized care, decision support, treatment outcomes, and quality of life.

A Latent Dirichlet Allocation (LDA) Semantic Text Analytics Approach to Explore Topical Features in Charity Crowdfunding Campaigns

Crowdfunding in the realm of the Social Web has received substantial attention, with prior research examining various aspects of campaigns, including project objectives, durations, and influential project categories for successful fundraising. These factors are crucial for entrepreneurs seeking donor support. However, the terrain of charity crowdfunding within the Social Web remains relatively unexplored, lacking comprehension of the motivations driving donations that often lack concrete reciprocation. Distinct from conventional crowdfunding that offers tangible returns, charity crowdfunding relies on intangible rewards like tax advantages, recognition posts, or advisory roles. Such details are often embedded within campaign narratives, yet, the analysis of textual content in charity crowdfunding is limited. This study introduces an inventive text analytics framework, utilizing Latent Dirichlet Allocation (LDA) to extract latent themes from textual descriptions of charity campaigns. The study has explored four different themes, two each in campaign and incentive descriptions. Campaign description’s themes are focused on child and elderly health mainly the ones who are diagnosed with terminal diseases. Incentive description’s themes are based on tax benefits, certificates, and appreciation posts. These themes, combined with numerical parameters, predict campaign success. The study was successful in using Random Forest Classifier to predict success of the campaign using both thematic and numerical parameters. The study distinguishes thematic categories, particularly medical need-based charity and general causes, based on project and incentive descriptions. In conclusion, this research bridges the gap by showcasing topic modelling utility in uncharted charity crowdfunding domains.

An AI-Driven Clinical Text-Based Decision Support System for Pancreatic Cancer Diagnosis

The high mortality rate of pancreatic cancer and its detection at a late stage require advanced early diagnostic methods. The proposed AI-based clinical decision support system that leverages natural language processing (NLP) to extract key information from medical texts, such as patient records and reports. Using machine learning algorithms, including recurrent neural networks and transformer models, the system aims to identify early signs of pancreatic cancer with high accuracy. This research aims to bridge the gap between the increasing complexity of medical data and the need for user-friendly diagnostic tools. The model focus on a text analytics approach, integrating NLP techniques such as named entity recognition and sentiment analysis with machine learning for predictive modeling. The system interface will help healthcare professionals make informed decisions with a great accuracy for treatment recommendations. By facilitating early detection and providing actionable insights, the model hopes to significantly reduce the burden of pancreatic cancer.

Identifying Key Issues in Climate Change Litigation: A Machine Learning Text Analytic Approach

As climate change, environmental, social, and governance (ESG), along with sustainability, become increasingly crucial for businesses and society, there is a noticeable scarcity of information and transparency regarding corporate practices. Often, government agency enforcement actions lead to litigation and are ultimately resolved by court decisions. Moreover, in instances when there is perceived inadequacy in government enforcement, citizens frequently turn to the courts for preventive judgments against businesses or agencies. In an effort to shed light on the multifaceted aspects of climate change, we adopted a novel, exploratory approach to analyze climate change-related litigation cases. Utilizing a blend of machine learning-based text analytics, we have extracted key insights from individual case narratives. Our analysis encompassed over four hundred cases from the Westlaw database through various keyword searches. The emergent topics from our case dataset revolved around four critical environmental themes: forest, land, water, and air emissions. Our findings provide insight into the nature and dimensions of climate change and also carry significant policy implications, laying the groundwork for future research in this domain.

Effects and challenges of the COVID-19 pandemic in supply chain management: a text analytics approach

ABSTRACT The coronavirus has had many effects on supply chains and logistics, most of which are negative. Due to the importance of logistics and supply chain in the world, any disruption or mismanagement causes many problems not only in the countries directly affected but also globally. In this article, new textual data are collected from reputable commercial and news websites related to the effects of COVID-19 on logistics and supply chains. After collecting textual data, valuable information about the impact of the coronavirus is extracted using various text mining techniques performed with R programming. Finally, issues related to COVID-19 and supply chains are identified and divided into five categories: suppliers and products, governments and organisations, health, evaluation, problems, and barriers. Also, categorising the problems and limitations of supply chains and logistics will provide managerial insights to minimise obstacles and disruptions. In particular, managers should consider several suppliers to reduce dependencies and also focus on domestic suppliers because of transportation limitations. Moreover, companies should pay attention to the health of societies and employ new policies, as well as pay attention to consumer behaviour such as their tendency to buy online.

Examining Public Perceptions of UK Rail Strikes: A Text Analytics Approach Using Twitter Data

Examining Public Perceptions of UK Rail Strikes: A Text Analytics Approach Using Twitter Data

Islamic rationality of Muslim consumers: new insight from text analytics and Al-Ghazali’s thought

PurposeThis paper aims to review 69 studies related to Muslim consumer behavior and determine the relationship between these topics and Islamic rationality. In addition, this paper elaborates on Al-Ghazali’s Islamic rationality model.Design/methodology/approachA text analytics approach is used to map 69 studies on Muslim consumer behavior. In addition, the historical-critical and inductive approach is used to identify Muslim scholars’ concepts and opinions regarding Islamic rationality, especially Al-Ghazali.FindingsThis study confirms that Muslim consumer behavior is in line with the concept of Islamic rationality proposed by Al-Ghazali. This is evidenced by a strong awareness of Islamic morals and values, which fosters a high commitment to halal products.Practical implicationsThe findings of this study will provide essential benefits in the development of Islamic rationality theory, which can then be used as an alternative in explaining Muslim consumer behavior and also can be used as a reference for stakeholders in the industry to mainstream halalfication on products offered in the Muslim market.Originality/valueThe value of originality in this study lies in identifying the relation between Islamic rationality and Muslim consumer behavior, and this effort was confirmed through 69 selected studies related to Muslim consumer behavior.

Historical Psychology

A growing body of evidence suggests that many aspects of psychology have evolved culturally over historical time. A combination of approaches, including experimental data collected over the past 75 years, cross-cultural comparisons, and studies of immigrants, points to systematic changes in psychological domains as diverse as conformity, attention, emotion, morality, and olfaction. However, these approaches can go back in time only for a few decades and typically fail to provide continuous measures of cultural change, posing a challenge for testing deeper historical psychological processes. To tackle this challenge most directly, computational methods emerging from natural language processing can be adapted to extract psychological information from large-scale historical corpora. Here, we first review the benefits of psychology as a historical science and then present three useful classes of text-analytic techniques for historical psychological inquiry: dictionary-based methods, distributed-representational methods, and human-annotation-based methods. These represent an excellent suite of methodologies that can be used to examine the record of “dead minds.” Finally, we discuss the importance of going beyond English-centric text analysis in historical psychology to foster a more generalizable and inclusive science of human behavior. We propose that historical psychology should incorporate and further develop a variety of text-analytic approaches to reliably quantify the historical processes that gave rise to contemporary social, political, and psychological phenomena.

Diversity, Networks, and Innovation: A Text Analytic Approach to Measuring Expertise Diversity

Diversity, Networks, and Innovation: A Text Analytic Approach to Measuring Expertise Diversity

Diversity, networks, and innovation: A text analytic approach to measuring expertise diversity

AbstractDespite the importance of diverse expertise in helping solve difficult interdisciplinary problems, measuring it is challenging and often relies on proxy measures and presumptive correlates of actual knowledge and experience. To address this challenge, we propose a text-based measure that uses researcher’s prior work to estimate their substantive expertise. These expertise estimates are then used to measure team-level expertise diversity by determining similarity or dissimilarity in members’ prior knowledge and skills. Using this measure on 2.8 million team invented patents granted by the US Patent Office, we show evidence of trends in expertise diversity over time and across team sizes, as well as its relationship with the quality and impact of a team’s innovation output.

Impacts of Precautionary and Opportunistic Buying Behaviors and Supply Issues on Supply Chain Resilience During the COVID-19 Pandemic

This paper proposes a text analytics approach using dictionary-based clustering, word counting data, and discrete regression modeling to study the relationship between demand behaviors and supply issues affecting supply chain resilience during the first stages of the COVID-19 pandemic. The work used news and media articles gathered from the LexisNexis database covering 5 months between February and July 2020. The data analyses describe the general patterns in the news texts by using text mining techniques, and the methodology describes the relationship between consumer behavior, supply chain issues, and the reduced level of service shown during the study period. Demand behaviors include precautionary and opportunistic buying, which affected many countries and could be the result of a lack of perceived control and other factors; for example, near-empty shelves of certain products could have prompted consumers to increasingly look for comparable products, driving up demand. Additionally, the method explored the potential effect of strategies to mitigate impacts on the resilience of supply chains. The results confirmed that buying behaviors and a reduction in the capacity of the supply chain led to a lower level of service being perceived by consumers, however, resilience strategies were found to mitigate the impact of such capacity reductions. Empirical analyses showed that the proposed approach, using data extracted from the news, could identify and represent impacts consistent with expectations from the supply chain field under disruptions, and quantify the magnitude of the impacts as the pandemic evolved, providing more information for decision making.

Mapping the Trend of Humor Studies in Educational Arena: A Text-analytic Approach

Humor is considered an educational method that can be implemented in educational processes with a wide range of educational variables. Examining the scope of research on educational humor has the potential to guide future studies. Therefore, the aim of this study is to show the conceptual trend in humor research in education over the past decade. 989 publications on the subject of humor in education were analyzed using the text mining method. A text analytic tool was used for the analysis, and the titles, abstracts, and keywords were included in the text analytics. As a result of the study, humor was studied in terms of various variables in different educational fields. In addition, the reflections of humor in education base on technological developments and the target groups applied were also revealed. The findings revealed the conceptual relationships and concept maps by year. It was concluded that the concept of humor in educational research is particularly related to the variables of motivation, attention, skill, creativity, engagement, and interest. Upon comparing 2010 and 2019, it has been observed that concepts related to online education, science, collaborative learning, and technology in humor studies have spread in recent years. In addition, humor has also emerged as a research phenomenon in language, health, and science education

Summarization tool for multimedia data

Text summarization is an important Natural Language Processing problem. Manual text summarization is a laborious and time-consuming task. Owing to the advancements in the field of Natural Language Processing, this task can be effectively moved from manual to automated text summarization. This paper proposes a model named Term Frequency-Inverse Document Frequency (TF-IDF) Summarization Tool which implements a text analytics approach called TF-IDF to generate a meaningful summary. TF-IDF is used to identify the topic or context of the text statistically. As data today is mostly unstructured in nature, this paper aims to explore a combination of NLP techniques such as Speech Recognition and Optical Character Recognition to summarize multimedia data as well. The TF-IDF Summarization Tool is seen to produce summaries with Jaccard's Similarity value of 67% and Rogue-1 of 64.9%, Rogue-2 of 48.2%, and Rogue-L of 56.4% based on a self-developed dataset.

The Accessibility of YouTube Fitness Videos for Individuals Who Are Disabled Before and During the COVID-19 Pandemic: Preliminary Application of a Text Analytics Approach.

BackgroundPeople with disabilities face barriers to in-person physical activity (PA), including a lack of adaptive equipment and knowledgeable instructors. Given this and the increased need for digital resources due to widespread COVID-19 lockdowns, it is necessary to assess the accessibility of digital fitness resources for people with disabilities. To investigate whether YouTube fitness content creators have made videos accessible to people with disabilities would be informative about access to PA during COVID-19 and could also provide insight into the feasibility of individuals who are disabled relying on YouTube for PA in a post–COVID-19 world.ObjectiveThis study aims to ascertain if disability-friendly PA videos on YouTube are accessible through searching general fitness terms and whether a change in the availability of accessible fitness resources for people with disabilities occurred on YouTube between before and during the COVID-19 pandemic on “Hospital/Medical Institutions,” “Individual(s),” and “Other(s)” channels. Secondary aims are to investigate if different categories of YouTube channels produce more accessible fitness content and highlight any disparities in disability-friendly PA content on YouTube.MethodsA cross-sectional text analysis of exercise-related YouTube videos was conducted. The authors used Python (version 3.0) to access the YouTube database via its data application programming interface. Terms pertaining to PA that were searched on YouTube were at-home exercise, exercise at home, exercise no equipment, home exercise, home-based exercise, no equipment workout, and workout no equipment. Various elements (eg, view count and content generation) of the videos published between January 1 and June 30, 2019 (n=700), were compared to the elements of videos published between January 1 and June 30, 2020 (n=700). To capture a broad idea of disability-friendly videos on YouTube, videos were labeled “accessible” if they were found in the first 100 video results and if their title, description, or tags contained the following terms: para, paralympic, adaptive, adapted, disabled, disability, differently abled, disability-friendly, wheelchair accessible, and inclusive. Each video and channel were categorized as “Hospitals/Medical Institutions,” “Individuals,” or “Other(s).”ResultsThe analysis revealed a statistically significant increase in viewership of fitness content on YouTube (P=.001) and in fitness content generated by Hospitals/Medical Institutions (P=.004). Accessible terms applicable to people with disabilities had minimal appearances in 2019 (21 videos) and 2020 (19 videos). None of the top viewed fitness videos that populated on YouTube from 2019 or 2020 were accessible.ConclusionsThe proportion of accessible disability-friendly videos remains diminutive relative to the prevalence of disability in the general population, revealing that disability-friendly videos are seldom findable on YouTube. Thus, the need for disability-friendly fitness content to be easily searched and found remains urgent if access to digital fitness resources is to improve.

Mining voice of customers and employees in insurance companies from online reviews: a text analytics approach

PurposeAlthough insurance companies contribute to about $600 billion of the US gross domestic product and employ 2.7 million people, the overall perception of clients and workers employed in this domain is poor. To the best of our knowledge, this study is the first to propose recommendations to improve the service quality of insurance companies using the voice of customers and employees that are available online.Design/methodology/approachThe proposed methodology consists of four stages: extracting text reviews posted by employees and customers, reviewing feedback using bigrams and trigrams, determining topics and identifying quality-associated managerial recommendations using the Strengths, Weaknesses, Opportunities and Threats (SWOT) and root cause analysis techniques.FindingsOur results indicate that primary causes for client dissatisfaction are improper client assistance services, inefficient claims processing, issues related to payments/fees and unresponsive ancillary services. Offering different types of payment methods, immediate car replacement policy and features such as smartphone applications are commended very well. The major reasons for workforce frustrations include long and overly complex training, lack of social events and incentive programs. Coworkers, workplace facilities, clean work environment and other amenities are shown to have a positive impact on employees' satisfaction level.Originality/valueTo the best of our knowledge, this study is the first to propose recommendations to improve the service quality of insurance companies using the voice of customers and employees that are available online. The proposed quality-related insights can assist insurance companies in improving the outlook of their workers as well as customers.