Text Analysis Tools Research Articles

Word Segmentation for Classical Chinese Buddhist Literature

With the growth of digital humanities, information technologies take on more important roles in humanities research, including the study of religion. To analyze text for further processing, many text analysis tools treat a word as a unit. However, in Chinese, there are no word boundary markers. Word segmentation is required for processing Chinese texts. Although several word segmentation tools are available for modern Chinese, there is still no practical word segmentation tool for Classical Chinese, especially for Classical Chinese Buddhist literature. In this paper, we adopt unsupervised and supervised learning techniques to build Classical Chinese word segmentation approaches for processing Buddhist literature. Normalized variation of branching entropy (nVBE) is adopted for unsupervised word segmentation. Conditional random fields (CRF) are used to generate supervised models for Classical Chinese word segmentation. The performance of our word segmentation approach achieves an F-score of up to 0.9396. The experimental results show that our proposed method is effective for correctly segmenting most Classical Chinese sentences in Buddhist literature. Our word segmentation method can be a fundamental tool for further text analysis and processing research, such as word embedding, syntactic parsing, and semantic labeling.

The Effects of Word Use and Personality Pathology on Depression and Suicidal Behavior in Older Adult Inpatients

Few prior studies have examined whether word use is associated with personality pathology or is linked to depression and suicidal behavior in older adult inpatients. In this study, older adult depressed inpatients (N = 51; age M= 67, SD = 8.9; mean years of education = 15.49, SD = 2.6) provided narratives about high and low points in life, health challenges, and interpersonal conflicts, and completed measures of personality pathology (NEO Five Factor Inventory, Inventory of Interpersonal Problems-Personality Disorders-25), depression (Geriatric Depression Scale), and suicidal ideation or behavior (SCID-5). The Linguistic Inquiry and Word Count (LIWC) text analysis tool was used to examine the frequency of self-referential, negative emotion, or absolutist word use. After adjusting for word count, age, and occupational and marital status, the use of self-referential words in narratives about high points of life predicted suicidal behavior (OR = .563, p = .002), but word use did not predict depression or personality pathology. In regression analyses, neuroticism and maladaptive interpersonal functioning predicted depression (ß = .417, p = .002; ß = .364, p = .009, respectively) but not suicidal behavior. Together, these preliminary findings suggest that in depressed older adult inpatients, depression and suicidal behavior may have qualitatively different predictors.

Emotional tones in scientific writing: comparison of commercially funded studies and non-commercially funded orthopedic studies

Background and purpose — There is ongoing debate as to whether commercial funding influences reporting of medical studies. We asked: Is there a difference in reported tones between abstracts, introductions, and discussions of orthopedic journal studies that were commercially funded and those that were not commercially funded? Methods — We conducted a systematic PubMed search to identify commercially funded studies published in 20 orthopedic journals between January 1, 2000 and December 1, 2019. We identified commercial funding of studies by including in our search the names of 10 medical device companies with the largest revenue in 2019. Commercial funding was designated when either the study or 1 or more of the authors received funding from a medical device company directly related to the content of the study. We matched 138 commercially funded articles 1 to 1 with 138 non-commercially funded articles with the same study design, published in the same journal, within a time range of 5 years. The IBM Watson Tone Analyzer was used to determine emotional tones (anger, fear, joy, and sadness) and language style (analytical, confident, and tentative). Results — For abstract and introduction sections, we found no differences in reported tones between commercially funded and non-commercially funded studies. Fear tones (non-commercially funded studies 5.1%, commercially funded studies 0.7%, p = 0.04), and analytical tones (non-commercially funded studies 95%, commercially funded studies 88%, p = 0.03) were more common in discussions of studies that were not commercially funded. Interpretation — Commercially funded studies have comparable tones to non-commercially funded studies in the abstract and introduction. In contrast, the discussion of non-commercially funded studies demonstrated more fear and analytical tones, suggesting them to be more tentative, accepting of uncertainty, and dispassionate. As text analysis tools become more sophisticated and mainstream, it might help to discern commercial bias in scientific reports.

Creating a Text Analysis Tool for Socio-Humanitarian Research. Part 2. The RSA Machine and the Experience in Using It

This paper considers the best-known tools for linguistic and statistical analysis of text corpora and introduces the RSA machine, which is a novel text analysis tool for socio-humanitarian research. The machine architecture and developing tools of the RSA machine are described. The results of a pilot study of texts using the RSA machine are presented. The pilot study of the capabilities of the new tool was carried out on the basis of 142 essay texts written on the theme “I. Others. World.” The performed correlation analysis revealed some relations between extracted texts attributes detected using the RSA machine (e.g. the frequency of predicate types and syntaxeme types) and psychodiagnostic research data, in particular, that on the level of aggressiveness. A scheme for interpreting predicate analysis data is proposed, which allows us to describe the specifics of speech-cognitive activity and worldview depending on the group and personal characteristics of the authors of the texts.

Cartographic Intertextuality: Reading The Narrative of Captivity and Restoration of Mrs. Mary Rowlandson with Geographic Information Systems

The Narrative of Captivity and Restoration of Mrs. Mary Rowlandson from 1682 is not only famous – or infamous – for its brutal descriptions of the armed conflicts of King Philip’s War, it is also a colonial document that contains both religious as well as spatial representations of Native American territories. This article proposes to analyze this entanglement of space and text with a combination of digital text analysis tools and geographic information systems (GIS). Applying the potentials of such technologies and methods to the study of captivity narratives like Mary Rowlandson’s opens up new opportunities to better understand the interaction of writing and space in colonial New England.

DIGITAL HUMANITIES: ЦИФРОВА ЕРА У ГУМАНІСТАРИСТИЦІ

The article considers the main directions in the field of applied methods of using Digital humanity, characteristics of leading research centers, laboratories and programs. Digital humanities projects are described at several levels that start with basic computing (programming, processing, and protocols) and go through the organization and production levels (interface, devices, networks). The methods of Digital humanity that apply to almost all types of cultural research and are related to cultural practices that use the soft Media language are considered: computer design, editing, creating databases (text, library, online encyclopedias, communication tools in the form of chats, messengers, social networks, etc.), and digital sociological, philological, historical, ethnographic research. The article examines in detail the phenomenon of digital humanities in the context of Peter Galison’s theory of “zones of exchange”, understood as “local coordination of beliefs and actions”. The aim of the study is to show that the methods of knowledge production working in the digital humanities influence the mechanisms of its institutionalization. The article is detailed the tool-knowledge-institutionalization relationship is analyzed and conclusions are made about current and potential models of institutionalization of this “exchange zone” in a foreign language contex.In the course of our research, we show that the digital humanities function as a kind of cognitive laboratory with virtual extensions in digital space. Considering the relationship of knowledge production strategies and tool development, we come to the conclusion that without an understanding of the nature of humanities knowledge, condensed inside digital analytical tools (e.g. text analysis tools), it is impossible to comprehend the epistemological vector of digital humanitarian research.

What’s in a text? Bridging the gap between quality and quantity in the digital era

The digital era has not only given us the world of big data, but the tools to deal with this mostly unstructured, mostly textual data: Natural Language Processing (NLP) tools. Yet, most humanists and social scientists do not work with big data. They do not deal with millions of documents. Literary critics’ corpora are the handful of works produced by an author. The historians’ primary source documents number in the tens, perhaps hundreds. Social scientists deal with tens or hundreds of transcripts of focus groups and in-depth interviews, or at most a few thousand media articles. And they analyze these data either qualitatively or quantitatively with a variety of manual or computer-assisted methodologies, from content analysis to frame analysis, discourse analysis, quantitative narrative analysis. But, once developed, at least some of the NLP tools of automatic textual analysis and the data analytics visualization tools, can be applied not just to big data but to small data as well. This paper illustrates how some of these tools can be used by focusing on a short first-person narrative. And the NLP tools reveal patterns of language use perhaps not immediately discernible, thus proving useful in the analysis of even small data. But understanding and interpreting these patterns requires knowledge way beyond the NLP tools themselves. Humanists and social scientists need not fear computer scientists; rather, they need to learn to take advantage of them. NLP tools lay a bridge between quality and quantity, with much to be gained from a constant interaction between distant and close reading.

Enhancing the content- and language-integrated multiple literacies framework: Systemic functional linguistics for teaching regional diversity

While content- and language-integrated instruction has been considered crucial for the development of literacy in a foreign language (FL) (e.g., Cammarata, Tedick, & Osborne, 2016), implementing an integrated approach is often challenging. This article demonstrates how systemic functional theory (SFL) (Halliday & Matthiessen, 2014) that establishes explicit connections between the linguistic and the social can be instrumental for integrated pedagogy design that promotes a critical orientation to text.Responding to the need to diversify content typically taught in German curricula, the article shows how instructors can expose learners to the multifaceted nature of the German cultural cosmos by focusing on two regions through Sorbian and Mecklenburg tales. Enhancing the pedagogy of multiliteracies (The New London Group, 1996), the article presents SFL-based concepts as tools for textual analysis and content- and language-integrated exploration of the texts.We demonstrate how instructional tasks based on the SFL analysis provide learners with a critical lens to examine how variation in the narrative structure and patterns of language use construe particular aspects of regional cultures and point to conceptualizations of human-nature relationships and gender roles that differ from traditional fairytales. In doing so, genre-based tasks help learners reflect on cultural representations and creatively apply linguistic and content knowledge.

Latent Dirichlet Allocation Model Training With Differential Privacy

Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for hidden semantic discovery of text data and serves as a fundamental tool for text analysis in various applications. However, the LDA model as well as the training process of LDA may expose the text information in the training data, thus bringing significant privacy concerns. To address the privacy issue in LDA, we systematically investigate the privacy protection of the main-stream LDA training algorithm based on Collapsed Gibbs Sampling (CGS) and propose several differentially private LDA algorithms for typical training scenarios. In particular, we present the first theoretical analysis on the inherent differential privacy guarantee of CGS based LDA training and further propose a centralized privacy-preserving algorithm (HDP-LDA) that can prevent data inference from the intermediate statistics in the CGS training. Also, we propose a locally private LDA training algorithm (LP-LDA) on crowdsourced data to provide local differential privacy for individual data contributors. Furthermore, we extend LP-LDA to an online version as OLP-LDA to achieve LDA training on locally private mini-batches in a streaming setting. Extensive analysis and experiment results validate both the effectiveness and efficiency of our proposed privacy-preserving LDA training algorithms.

Defragmenting Research Areas with Knowledge Visualization and Visual Text Analytics

The increasing specialization of science is motivating the fragmentation of traditional and well-established research areas into interdisciplinary communities of practice that focus on cooperation between experts to solve problems in a wide range of domains. This is the case of problem-driven visualization research (PDVR), in which groups of scholars use visualization techniques in different application domains such as the digital humanities, bioinformatics, sports science, or computer security. In this paper, we employ the findings obtained during the development of a novel visual text analytics tool we built in previous studies, GlassViz, to automatically detect interesting knowledge associations and groups of common interests between these communities of practice. Our proposed method relies on the statistical modeling of author-assigned keywords to make its findings, which are demonstrated in two use cases. The results show that it is possible to propose interactive, semisupervised visual approaches that aim at defragmenting a body of research using text-based, automatic literature analysis methods.

Complexity, resources and text borrowing in state legislatures

AbstractDo states copy or reinvent language from complex policies as they diffuse, and does this depend on legislative resources? We argue that states will more frequently reinvent more complex policies, but that states with high-resource legislatures will reinvent more than their low-resource counterparts for more complex policies. We test the theory using the bill texts from 18 policies that diffused across the 50 states from 1983 to 2014, measuring reinvention and complexity using text analysis tools. In line with expectations, we find that complex policies are reinvented more than simple policies and that high-resource legislatures reinvent bills more than low-resource legislatures on average. However, we also find that low-resource legislatures reinvent complex policies at about the same rate as high-resource legislatures. The results indicate that even legislatures with limited resources work to adapt complex policies during the diffusion process.

Innovative drivers for family business models in tourism

PurposeThe aim of this paper is to verify whether, in the tourism sector, the “family business model” is an important development opportunity and, in particular, if it is an innovation driver for this industry development. In the literature, there is no conclusive evidence of this for the tourism sector. In this context, the authors investigate personal and family needs and preferences alongside the relationship between family business model, growth and profit maximization and the development of tourism businesses through innovation drivers.Design/methodology/approachTo develop this topic, the authors conducted an extensive literature review considering the scientific papers published and contained mainly in database in the last 10 years (2010–2020) and focused the attention on the last five years. The authors ran content and structural analysis on the collected sources by main scientific databases (EBSCO, Scopus, Thomson Reuter, etc.). Based on a systematic literature review, the analysis was conducted using statistical criteria and bibliometric indicators. In detail, the authors used systematic literature review, bibliometric analysis and automatic text analysis (ATA) tools for identified lexicon analysis and strategic keywords and used statistical correlation to classify the different approaches in the literature and to outline the orientations of the various research groups.FindingsFrom this analysis, the correlation between tourism, hospitality, entrepreneurship, life cycle and innovation dynamics was analysed. Important research gaps are identified, and future research priorities are suggested. Implications for both family business and tourism theory are discussed.Originality/valueWhile the intersection between tourism management and family business model has been established in the literature, the number of related publications is still limited. Against this background, a literature review as a total analysis was an adequate and practicable research methodology. This paper proposes a comprehensive literature review and a reflection on the potential developments and applications for family business in the tourism sector. Authors also suggest several research directions that have not been adequately investigated yet. In particular, scholars do not seem to have caught all the implications of innovation adoption, especially for SMEs and family ownerships in tourism.

Exploring the relationship between clout and cognitive processing in MOOC discussion forums

AbstractThe purpose of this study was to explore the relationship between clout and cognitive processing in massive open online course (MOOC) discussion forum posts. Cognitive processing, a category variable generated by the automated text analysis tool, Linguistic Inquiry Word Count (LIWC), is made up of six sub‐scores (insight, causation, discrepancy, tentativeness, certainty and differentiation). Clout is a nontransparent summary variable in LIWC that can be used to understand the level of confidence conveyed in the text. Because clout is nontransparent, we do not know the algorithm used to calculate its value. To better understand this variable, this study examined cognitive processing alongside clout. In this study, a series of linear mixed models were fitted to evaluate, after controlling for gender, degree and type of post, if the focal relationship between each sub‐score of cognitive processing as the predictor variables and clout as the dependent variable changed across courses with different pacing (self‐paced or instructor‐paced). Next, the focal relationship between each predictor and clout was examined with or without regard to pacing. Findings showed words classified as showing discrepancy, certainty or differentiation were negatively associated with clout scores.

Using Computational Text Analysis Tools to Study African Online News Content

After radio and television, online media are fast becoming a primary source of information for many Africans. With this increase, it is becoming necessary for media researchers to explore ways to better understand production, content and reception patterns of online news in the continent. This paper introduces freely available tools for systematic and (semi-)automated collection, storage and analysis of digital news that builds on recent advances in the computational power of personal computers, and the decreasing costs of storing large amounts of data. I start by describing existing challenges in the collection of online news text data, including the limited amount of African news content in commercial databases, and the methodological shortcomings of using commercial search engines. Then, I present a four-stage approach using packages written in the open-source R programming language to automate the collection of online news content (web scraping); transform this content for easier storage and analysis (data processing); use computational text analysis tools to describe and categorise data; and present the results in ways that are easier to understand (data visualisation). The paper concludes with a summary of recommendations for using computational methods to study African communication phenomena.

Migration Discourse in Sweden: Frames and Sentiments in Mainstream and Social Media

This study undertakes a systematic analysis of media discourse on migration in Sweden from 2012 to 2019. Using a novel data set consisting of mainstream newspapers, Twitter and forum data, the study answers two questions: What do Swedish media actually talk about when they talk about “migration”? And how do they talk about it? Using a combination of computational text analysis tools, I analyze a shift in the media discourse seen as one of the outcomes of the European refugee crisis in 2015 and try to understand the role of social media in this process. The results of the study indicate that messages on social media generally had negative tonality and suggest that some of the media frames can be attributed to a migration-hostile discourse. At the same time, the analysis of framing and sentiment dynamics provides little evidence for the discourse shift and any long-term effects of the European refugee crisis on the Swedish media discourse. Rather, one can hypothesize that the role of the crisis should be viewed in a broader political and historical context.

An Interactive Dialogue Agent to Assist African American and Hispanic/Latino Heart Failure Patients with Self-Care Needs

Introduction Heart failure (HF) is a public health problem, and self-care remains poor, especially among minorities. Access to personalized educational material via an interactive dialogue agent (DA), a system intended to converse with humans in natural language, has the potential to improve self-care. This abstract outlines the initial steps of developing an artificial intelligence-based DA to assist African American (AA) and Hispanic/Latino (H/L) HF patients with their self-care needs. Hypothesis Analysis of HF education sessions between health educators (HE) and patients can provide insight into the topics that AA and H/L patients value. Method In this IRB approved pilot study, we have recorded, transcribed, and verified 20 (18 AA and 2 H/L) HF education sessions between HE and HF patients. Latent Dirichlet Allocation (LDA), a topic modeling algorithm and Empath, a text analysis tool were used to identify common discussion topics in the transcripts. An initiative analysis was performed to identify conversation drivers where each turn (unit of speech by a single speaker, without interruption from the other speaker) was classified as either a question (ending with ‘?’), a prompt (having only filler words like umm, okay) or an assertion/command (others). Lastly, we compared the transcripts against a HF ontology published by the National Center for Biomedical Ontology and the Consumer Health Vocabulary (CHV) available in the Unified Medical Language System (UMLS) to identify the term overlap. Results On average, HE took 117 turns comprising 205 sentences and 2281 words per conversation, whereas patients took 108 turns comprising 131 sentences and 850 words. Per conversation, HE asked 26 questions and had 17 prompts as opposed to 3 questions and 39 prompts by the patients. LDA identified HF and heart function, effects of HF, low salt diet, and follow-up appointment and medication as the top 4 most common topics discussed by the HE. When looking at the entire transcripts, Empath identified eating, health, and cooking as the most common topics for both the HE and the patients. Patients frequently discussed children and family, whereas HE focused on providing HF information as indicated by LDA. Lastly, only 2.1% of HE terms overlapped with the HF ontology. Our analysis revealed that 25% and 22% of the terms (without stop words such as at, the) used by the patients and HE respectively match with the ‘preferred label’ in the CHV. For both, the high frequency terms included heart, heart failure, salt, water, and fluid. This also correlated with our topic analysis findings from Empath and LDA. Conclusion Our analysis helps triangulate the kind of information HE and HF patients value. Though mostly HE dominated the conversation, it is essential to incorporate the topics that patients brought up into the DA and use culturally sensitive vocabulary while communicating them. Next, we will collect more recordings, conduct focus groups, and evaluate the usability of our prototype DA.

Going Social: A Comparative Analysis of Presidents' Official and Social Media Messages

AbstractThis article explores how structural changes to the media environment, such as the development of social media, might enhance or impede presidents' public leadership efforts. We argue that characteristics of Twitter as a medium provide presidents with incentives to exploit these new opportunities and alter the content of presidential rhetoric. To test this claim about presidential rhetoric, we use text analysis tools to compare the readability, sentiment, and content of contemporary presidents' messages from press conferences and exchanges with reporters to tweets from the Barack Obama and Donald Trump presidencies. Our findings suggest that social media is being used by presidents in their public leadership efforts and that the medium of communication has a significant influence on the content of communication.

Disclosing Influence: Hydraulic fracturing, interest groups, and state policy processes in the United States

This paper examines copy-and-paste regulating in hydraulic fracturing (HF) fluid disclosure regulation across US states. Using text analysis, cluster analysis and document coding, we compare HF regulations of twenty-nine states and two “model bills” drafted by the conservative American Legislative Exchange Council (ALEC) and the Environmental Defense Fund (EDF, an environmental NGO). In contrast to recent studies that have documented ALEC’s widespread influence across policy domains, we find limited evidence of ALEC influence in HF fluid disclosure regulations. Instead, elements of the EDF bill are more prevalent across state regulations. Yet, text similarity scores between states are higher than similarity scores between states and the EDF bill. In particular, Colorado and to a lesser extent Pennsylvania functioned as leader states for other states to follow. This indicates that state-to-state communication was a more influential channel of policy diffusion than interest group model bills in this instance. Future research should better examine processes of information sharing amongst state oil and gas regulators as regulatory text is but one channel of policy diffusion. The cluster analysis also reveals that contiguous states, often within the same shale basins, frequently have different regulations. This finding calls for a reconsideration of the current state-led environmental regulatory framework for HF, which has resulted in a patchwork of regulations across the US. Finally, through the use of novel text analysis tools, this paper adds methodological diversity to the study of policy diffusion within energy policy.

Средства анализа текстов на основе криптоанализа простой замены

The article considers text analysis tools based on a numerical estimate obtained using the cryptographic approach. It contains applying frequency cryptanalysis methods of one-to-one substitution to plaintext. In this case, these methods tend to methods for identifying letters of the text, and the task of cryptanalysis is to the task of identifying letters of text. For frequency cryptanalysis methods of one-to-one substitution, the identification error is determined as a statistic of a number of cryptanalysis errors for certain volumes of texts. The numerical score called the quality factor of a method can be transferred from methods to texts. Text analysis based on the quality factor of texts includes the selection of a cryptographic method with the help of which a quantitative assessment of the text is given, and the subsequent calculation of the quality factor of the text vector. In order to analyze texts based on cryptanalysis of a simple substitution the main stages of this analysis and necessary means are determined. These tools are implemented in the graphical user interface of MS Access and include 30 navigation tabs, on which 143 information and control elements are located. The article includes examples of implementation of some tools.

Grounded reality meets machine learning: A deep-narrative analysis framework for energy policy research

Text-based data sources like narratives and stories have become increasingly popular as critical insight generator in energy research and social science. However, their implications in policy application usually remain superficial and fail to fully exploit state-of-the-art resources which digital era holds for text analysis. This paper illustrates the potential of deep-narrative analysis in energy policy research using text analysis tools from the cutting-edge domain of computational social sciences, notably topic modelling. We argue that a nested application of topic modelling and grounded theory in narrative analysis promises advances in areas where manual-coding driven narrative analysis has traditionally struggled with directionality biases, scaling, systematisation and repeatability. The nested application of the topic model and the grounded theory goes beyond the frequentist approach of narrative analysis and introduces insight generation capabilities based on the probability distribution of words and topics in a text corpus. In this manner, our proposed methodology deconstructs the corpus and enables the analyst to answer research questions based on the foundational element of the text data structure. We verify theoretical compatibility through a meta-analysis of a state-of-the-art bibliographic database on energy policy, narratives and computational social science. Furthermore, we establish a proof-of-concept using a narrative-based case study on energy externalities in slum rehabilitation housing in Mumbai, India. We find that the nested application contributes to the literature gap on the need for multidisciplinary methodologies that can systematically include qualitative evidence into policymaking.