Text Analysis Tools Research Articles

통역 교육에서의 원문 텍스트 난이도 평가 자동화 : 코메트릭스(Coh-Metrix)를 이용한 다차원적 텍스트 난이도 평가를 중심으로

While text difficulty is one of the critical considerations in developing and selecting materials for interpreter training, its assessment is a challenging task due to the complexities involved in analyzing a large number of text characteristics. The recent developments in automated text analysis methods and tools are expected to alleviate the burden of individual teachers and students assessing source text difficulty by aiding their decision making process with the immediate provision of data on the objective and quantifiable text dimensions. This study presents three cases of applying automated assessment of source text difficulty based on the five Text Easability Principal Component Scores provided by Coh-Metrix, a widely-used automated tool for multilevel analyses of text characteristics. For the purpose of the study, source texts used for interpreter training in Korea and China were sampled to conduct text difficulty assessment with Coh-Metrix measures and its visualization tool. The study discusses the potential benefits of automated analysis of multidimensional text difficulty for selecting the appropriate materials for effective interpreter training.

Assessment of a Mindfulness Meditation Elective in an Accelerated Doctor of Pharmacy Program

Objective. The objective of this study was to determine the effect of a live 14-week mindfulness elective course on the well-being of Doctor of Pharmacy (PharmD) students in an accelerated program.Methods. Pharmacy students enrolled in a mindfulness elective participated in weekly class sessions that included an eight-week mindfulness program geared toward emerging adults. Eight weekly reflections were assigned to students and evaluated using the Text iQ text-analysis tool in Qualtrics. Investigators analyzed the sentiment scores assigned by Text iQ to detect differences in the tone of student reflections over time.Results. Twenty-four students were enrolled in this elective, and 22 students submitted complete reflections for evaluation. Mean sentiment scores and the percentage of responses in sentiment score categories (very positive and positive, mixed and neutral, very negative and negative) for these reflections showed significant differences between weeks.Conclusion. The tone of student reflections was more positive after the students learned and incorporated mindfulness practice into their accelerated PharmD curriculum.

Effects of Information Overload on Financial Markets: How Much Is Too Much?

Motivated by cognitive theories verifying that investors have limited capacity to process information, we study the effects of information overload on stock market dynamics. We construct an information overload index using textual analysis tools on daily data from The New York Times since 1885. We structure our empirical analysis around a discrete-time learning model, which links information overload with asset prices and trading volume when investors are attention constrained. We find that our index is associated with lower trading volume and predicts higher market returns for up to 18 months, even after controlling for standard predictors and other news-based measures. Information overload also affects the cross-section of stock returns: Investors require higher risk premia to hold small, high beta, high volatile, and unprofitable stocks. Such findings are consistent with theories emphasizing that information overload increases information and estimation risk and deteriorates investors' decision accuracy amid their limited attention.

The writing is on the wall: predicting customers' evaluation of customer-firm interactions using computerized text analysis

PurposeThis methodological paper demonstrates how service firms can use digital technologies to quantify and predict customer evaluations of their interactions with the firm using unstructured, qualitative data. To harness the power of unstructured data and enhance the customer-firm relationship, the use of computerized text analysis is proposed.Design/methodology/approachThree empirical studies were conducted to exemplify the use of the computerized text analysis tool. A secondary data analysis of online customer reviews (n = 2,878) in a service industry was used. LIWC was used to conduct the text analysis, and thereafter SPSS was used to examine the predictive capability of the model for the evaluation of customer-firm interactions.FindingsA lexical analysis of online customer reviews was able to predict evaluations of customer-firm interactions across the three empirical studies. The authenticity and emotional tone present in the reviews served as the best predictors of customer evaluations of their service interactions with the firm.Practical implicationsComputerized text analysis is an inexpensive digital tool which, to date, has been sparsely used to analyze customer-firm interactions based on customers' online reviews. From a methodological perspective, the use of this tool to gain insights from unstructured data provides the ability to gain an understanding of customers' real-time evaluations of their service interactions with a firm without collecting primary data.Originality/valueThis research contributes to the growing body of knowledge regarding the use of computerized lexical analysis to assess unstructured, online customer reviews to predict customers' evaluations of a service interaction. The results offer service firms an inexpensive and user-friendly methodology to assess real-time, readily available reviews, complementing traditional customer research. A tool has been used to transform unstructured data into a numerical format, quantifying customer evaluations of service interactions.

A role by any other name: A content analysis of digital asset management on library and archives job boards

A digital asset manager curates the sum of an institution’s knowledge in the form of digital assets. A cursory review of job advertisements reinforces the ubiquity of digital asset management (DAM) in library and archival contexts. However, it also reveals great variability in how this specialised work is articulated in advertisements, the credentials sought by employers and the salaries offered. This study assesses the state, scope and attributes of DAM positions using a content analysis of 98 unique job advertisements for DAM positions collected from library and archive job boards over a six-month period. Voyant, an open source text analysis tool, was utilised to yield word and phrase frequencies to aid interpretation of scope of work, position expectations and education requirements. The results provide insights to aid organisations in recruiting DAM librarians and archivists, guide students in forging career paths in DAM, and inform development of the DAM curriculum in higher education. To meet the needs of the field, the authors recommend clarity in DAM job scopes, developing technological and personnel capacity to support the work, and calibrating compensation among employment sectors to elevate the importance of the work and promote equity across the profession.

In Search of Sustainability and Financial Returns: The Case of ESG Energy Funds

The world is facing several challenges, and the problem of sustainable development is one of the most important. It is worth considering that European countries are playing a significant role as pioneers in building a sustainable world, such as those promises made by signing the Paris Agreement and European Taxonomy. To achieve ambitious targets within sustainable development, a huge amount of capital is necessary, while financial and capital market participants are expected to demonstrate a high level of engagement in the domain of sustainability. Facing growing interest and demand, a relatively new product—the ESG (environmental, social, and governance) investment fund—was introduced. Scientific literature is providing some controversial views regarding the overall evaluation of this product. Therefore, additional research providing different angles would contribute to a better understanding. This study examines European ESG funds in the energy sector, from the perspective of news flows and investors. It is worth noting that the authors use the word “European” to refer to members of the European Union (EU). The paper consists of the following parts. In the introduction, the current state of this issue is discussed. The following section offers a literature review and a news flow analysis that contributes to a deeper understanding of these issues. A description of the methodology applied for the data analysis follows this, and the final section presents the research results and conclusions. The authors apply statistical analysis and the Carhart model to determine the differences in the performance of the ESG and conventional funds and use their own tool for text analysis to examine the relevance of the topic of ESG to attract client interest. The authors claim that the performance of the European ESG equity funds do not show a statistically significant difference from the non-ESG equity funds in the majority of the periods examined. The application of the adjusted Carhart model demonstrates that the factor of sustainability has a non-significant and negative effect on the fund performance. Finally, the authors highlight the urgent necessity for the unified usage of keywords and terminology, such as “ESG”, “sustainability”, etc., to ensure comparison and attribution possibilities.

Computerized corpus as a tool for educational technology and learning in the analysis of four-word recurrent expressions

The study aimed to investigate the disciplinary variations and the structural/functional features of recurrent expressions in the introduction and literature sections of the Master of Arts (MA) theses in two corpora. It is significant since the bundles fulfil specific functions in the form of formulaic language. The sampling of the study was Turkish and British academic writers. Their essays mainly based on academic argumentative topics were selected as analysis data. Data collection included compilation and categorization of the computerized corpora for possible differences and similarities. Sketch Engine, an online text analysis tool, was used for the analysis. Exploring the usage patterns of recurrent expressions among the native and non-native corpora, we also analysed the four-word recurrent expressions and featured the existing variations of functional and structural aspects. Cross-linguistic analysis revealed that Turkish writers distinguish some features of recurrent expressions more than others Structural expressions encompassed the verb-based, noun and propositional structures. The research, text, and participant oriented recurrent expressions built up the functional groups. The most frequently used functional categories were location and description in research-oriented recurrent expressions. The findings also indicated a positive role of recurrent expressions in the learning of the disciplinary features. Structural similarities were also found, indicating strong functional features for constructing discourse in research writing. The implication is that creating appropriate academic discursive practices with a focus on the properties of the recurrent expressions can be better analysed by employing several online corpus tools.

Mediating the Truth: Influences of Routines on Legacy News Media Fact-Checking

ABSTRACT This study analyzed fact-check stories (N = 1,530) published in four legacy news media organizations in the United States – The Associated Press, CNN, The New York Times, and The Washington Post – in 2017, 2018, and 2019 to understand: (1) which topics are more prominent; (2) what or whose claims are being fact-checked; and (3) sources of information used to adjudicate claims. It used a set of automated text analysis and machine learning tools to analyze the stories. Findings show that legacy news media’s fact-check coverage focuses primarily on the U.S. presidency and popular policy topics, and depends heavily on routine sources.

Social support on Reddit for antiretroviral therapy

PurposeSocial media platforms such as Reddit can be used as a place for people with shared health problems to share knowledge and support. Previous studies have focused on the overall picture of how much social support people who live with HIV/AIDS (PLWHA) receive from online interactions. Yet, only few studies have examined the impact of social support from social media platforms on antiretroviral therapy (ART), which is a necessary lifelong therapy for PLWHA. This study used social support theory to examine related Reddit posts.Design/methodology/approachThis study used content analysis to analyze ART-related Reddit posts. Each Reddit post was manually coded by two coders for social support type. A computational text analysis tool, Linguistic Inquiry and Word Count, was used to generate linguistic features. ANOVA analyses were conducted to compare differences in user engagement and well-being across the types of social support.FindingsResults suggest that most of the posts were informational support posts, followed by emotional support posts and instrumental support posts. Results indicate that there are no significant differences within user engagement variables, but there are significant differences within several well-being variables including analytic score, clout score, health words usage and negative emotional words usage among social support types.Originality/valueThis study contributes to further understanding of social support theory in an online context used predominantly by a younger generation. Practical advice for public health researchers and practitioners is discussed.

Turtling: a time-aware neural topic model on NIH grant data.

Recent initiatives for federal grant transparency allow direct knowledge extraction from large volumes of grant texts, serving as a powerful alternative to traditional surveys. However, its computational modeling is challenging as grants are usually multifaceted with constantly evolving topics. We propose Turtling, a time-aware neural topic model with three unique characteristics. First, Turtling employs pretrained biomedical word embedding to extract research topics. Second, it leverages a probabilistic time-series model to allow smooth and coherent topic evolution. Lastly, Turtling leverages additional topic diversity loss and funding institute classification loss to improve topic quality and facilitate funding institute prediction. We apply Turtling on publicly available NIH grant text and show that it significantly outperforms other methods on topic quality metrics. We also demonstrate that Turtling can provide insights into research topic evolution by detecting topic trends across decades. In summary, Turtling may be a valuable tool for grant text analysis. Turtling is freely available as an open-source software at https://github.com/aicb-ZhangLabs/Turtling.

ІНФОРМАЦІЙНО-АНАЛІТИЧНА СИСТЕМИ ПРОГНОЗУВАННЯ ВРАЗЛИВОСТЕЙ ТА ЗАГРОЗ ІНФОРМАЦІЙНОЇ БЕЗПЕКИ

The paper proposes a block diagram of an information-analytical system for predicting vulnerabilities and threats to information security. The analysis of the conducted research allows us to conclude that in order to solve the problem of research and development of an information-analytical fuzzy system for a logical fuzzy conclusion about the emergence of vulnerabilities and threats to information security, automating the analysis of the message flow of thematic Internet resources, it is advisable to use expert forecasting systems. To solve the problems of predicting vulnerabilities and threats to information security of confidential data based on the flow of thematic messages of Internet resources using the proposed algorithms and method, hybrid-type expert forecasting systems designed for use on general-purpose information and computer technology can be used. The algorithm for predicting vulnerabilities and threats to information security implemented in the information and analytical system based on the analysis of the data flow of thematic Internet resources allows automating the information process of detecting new vulnerabilities and threats, provides information security specialists with the opportunity to assess the degree of security of resources in a timely manner and, if necessary, take appropriate measures for neutralization possible threats and vulnerabilities, thereby increasing the information security of computing computer systems against the implementation of new network computer attacks. An analysis of fuzzy logical inference systems, modern tools for processing large volumes of data, tools for morphological text analysis, and ontology editors was conducted. UML diagrams of activities, sequences of actions, and classes were built to carry out logical modeling of the information system for forecasting vulnerabilities and threats to information security. For the physical modeling of the system, UML-diagrams of deployment and components have been developed. The possibility of implementing an information-analytical system for predicting vulnerabilities and threats to information security based on the analysis of text messages of thematic Internet resources using the following software products is substantiated: DBMS MySQL, ontology editor - Protégé, fuzzy logic inference system - Fuzzy Logic Designer, morphological data analysis tools - Mystem. To evaluate the obtained results, the indicators MAPE, MAE, RMSE for the values of forecasting the occurrence of vulnerabilities and information security threats, as well as smoothed time series calculated on their basis with a period of three and five.

Exploring the effect of digital transformation on Firms’ innovation performance

The influence of digital industry and firm digitization on enterprise innovation has emerged as a critical research topic. To assess the impact of digital transformation on enhancing innovation output, we propose a game model of two organisations investing in digital transformation, analyze the index of enterprise digitalization level with Python tools for text analysis, and employ a fixed effect model. The findings indicate that firm digitalization and the level of regional digital industry innovation can both promote firm innovation. However, the regional digital industry innovation level can have a negative moderating effect on the firm digitalization innovation effect. Furthermore, the impact of firm digitalization on innovation is more visible in digital-related service industries. In other industries, the regional digital industry innovation level has a greater impact on innovation promotion. Due to firms' free-riding tendency in technology adoption, this study shows that the higher the level of digital industrialization in the region where the firm is located, the lower the marginal innovation efficiency of the firm's digital investment. When the level of development of digital industrialization in the region where a firm is located is higher, the "competitive effect" improves the marginal innovation efficiency of firms in adjacent areas, implying that digital industrialization has a spatial spillover effect. The relevant robustness test further verifies the conclusion of the empirical analysis. As a result, the digital industry should be given more attention and financial support.

Kto, kogo i po co kołysze? Kołysanki rodzicielskie w kulturze polsko-żydowskiej przełomu XIX i XX wieku

Who Rocks Whom to Sleep and Why? Parental Lullabies in Polish-Jewish Culture at the Turn of 19th Century The purpose of this article is to analyze the communicative situation in selected Jewish and Polish-Jewish lullabies from the turn of the 20th century. The main criterion for the selection is the person of the speaking subject – successively a man and a woman, while the most important contexts of their statements include the role of the father and mother in the Jewish family, the way of coping with traumatic experiences and the absence of the partner, the subjectivity of the rocking person, and the national identity of the authors of the lullabies. The conclusion to be drawn from the analysis of the works is a change in the view of the lullaby function, which from a piece of trivial content intended to put children to sleep becomes a work that serves adults, with a clear self-therapeutic emphasis. Beyond poetological tools for text analysis, the work also includes references to psychological and musicological concepts, justified by the specifics of the research undertaken.

중학교 영어듣기평가 문항 특성 분석

The purpose of the current study is to compare and contrast the two standardized middle school English listening tests; the National test and the Seoul test. To compare the test characteristics of the 20 items for each test, the speech rate as well as 9 linguistic features selected from Coh-Metrix (a web-based text-analysis tool) were analyzed. Coh-Metrix measures were analyzed under the following categories: basic counts, lexical complexity, syntactic complexity, and discourse coherence. 60 middle school students participated in this study to compare the test results and the overall difficulty. To calculate the speech rate (WPM), Praat and MS Excel were used, and Coh-Metrix was used to compare the aforementioned linguistic measures of each listening test. The test results revealed that the average score of the National type was higher than that of the Seoul test. The analysis on the speech rate demonstrated that the average speech rate of both test types belonged to ‘Moderately Slow’ based on the standard speech rate classification (Pimsleur, Hancock, & Furey, 1977); however, the speech rate of the Seoul test was found to be significantly higher. Most of the linguistic features analyzed were about the similar measures. Pedagogical implications will be discussed.

An exploratory study of social media's role in facilitating public participation in e‐rulemaking using computational text analysis tools

AbstractGovernment agencies struggle to use social media effectively to engage and involve the public. However, we know little about how individuals and groups on the other side of these efforts actually use social media to participate in government decision‐making processes, independent of government‐led engagement efforts. This exploratory study aims to address this by looking closer at how Twitter users self‐mobilize after a particular event in which they may be motivated to participate. Specifically, the study examines Twitter activity before and after the United States Department of the Interior proposed changes to its Freedom of Information Act (FOIA) regulations that would limit public access to agency records. Acomputational text analysis of 5000 tweets using #FOIA over a 99‐day period showed significant changes in the sentiment of the dialog before and after the proposed rule was announced. Furthermore, structural topic models show evidence of a diverse set of stakeholders using Twitter to exercise voice and share information about the proposed rule. Findings suggest that citizens and interest groups are taking to Twitter to mobilize participation in the rulemaking process prompting the need for more research on how the public uses social media to influence government decision‐making, absent of government‐led engagement efforts.

A Model for Automated Business Writing Assessment

This study is aimed at building an automated model for business writing assessment, based on 14 rubrics that integrate EFL teacher assessment frameworks and identify expected performance against various criteria (including language, task fulfillment, content knowledge, register, format, and cohesion). We developed algorithms for determining the corresponding numerical features using methods and tools for automatic text analysis. The algorithms are based on a syntactic analysis with the use of dictionaries. The model performance was subsequently evaluated on a corpus of 20 teacher-assessed business letters. Heat maps and UMAP results represent comparison between teachers’ and automated score reports. Results showed no significant discrepancies between teachers’ and automated score reports, yet detected bias in teachers’ reports. Findings suggest that the developed model has proved to be an efficient tool for natural language processing with high interpretability of the results, the roadmap for further improvement and a valid and unbiased alternative to teachers’ assessment. The results may lay the groundwork for developing an automatic students’ language profile. Although the model was specifically designed for business letter assessment, it can be easily adapted for assessing other writing tasks, e.g. by replacing dictionaries.

A Corpus-based Study on Selected Dalit Autobiographies

Dalit literature represents consciousness about human rights. Autobiographies written by Dalit authors are based on real-life experiences. Based on the conducted studies regarding Dalit autobiographies, women are the ones who suffer oppression and violence to a great extent (Simon, 2021) since they are the backbone of the family. Therefore, how Dalit autobiographies draw their reader’s attention is essential and makes these autobiographies unique. In recent years, text analysis tools have been introduced to facilitate information extraction from a collection of texts and compare two or more texts. So, this article using text mining tools aims to analyze three selected Dalit autobiographies of women writers. The central hypothesis of this study is that sufferers, their families and society have higher frequency in Dalit autobiographies and semantic relations between these elements at the lexical level are available. Based on the findings of this study, only in Dutt’s work, keyword in contexts (KWICs) such as “Dalit, caste, India” had higher frequencies than two other works. In Halder’s work, the frequency of these KWICs was zero. Accordingly, it can be said that in recent years, authors are explicitly willing to attract their readers’ attention toward Dalits and their problems.

OP74 Assessing Public Confidence Towards COVID-19 Vaccines Through Social Media Insights Leveraged Using Artificial Intelligence Techniques

IntroductionIn areas where public confidence is low and there is a lack of understanding around behaviors, such as COVID-19 vaccine hesitancy, there is a need to explore novel sources of evidence. When leveraged using artificial intelligence (AI) techniques, social media data may offer rich insights into public concerns around vaccination. Currently, sources of ‘soft-intelligence’ are underutilized by policy makers, health technology assessment (HTA) and other public health research agencies. In this work, we used an AI platform to rapidly detect and analyze key barriers to vaccine uptake from a sample of geo-located tweets.MethodsAn AI-based tool was deployed using a robust search strategy to capture tweets associated with COVID-19 vaccination, posted from users in London, United Kingdom. The tool’s algorithm automatically clustered tweets based on key topics of discussion and sentiment. Tweets contained within the 12 most populated topics with negative sentiment were extracted. The extracted tweets were mapped to one of six pre-determined themes (safety, mistrust, under-representation, complacency, ineffectiveness, and access) informed using the World Health Organization’s 3Cs vaccine hesitancy model. All collated tweets were anonymized.ResultsWe identified 91,473 tweets posted between 30 November 2020 and 15 August 2021. A sample of 913 tweets were extracted from the twelve negative topic clusters. Of these, 302 tweets were coded to a vaccine hesitancy theme. ‘Safety’ (29%) and ‘mistrust’ (23%) were the most commonly coded themes; the least commonly coded was ‘under-representation’ (3%). Within the main themes, adverse reactions, inadequate assessment, and rushed development of the vaccines as key findings. Our analysis also revealed widespread sharing of misinformation.ConclusionsUsing an AI-based text analytics tool, we were able to rapidly assess public confidence in COVID-19 vaccination and identify key barriers to uptake from a corpus of geo-located tweets. Our findings support a growing body of evidence and confidence surrounding the use of AI tools to efficiently analyze early sources of soft-intelligence evidence in public health research.

Paralanguage Classifier (PARA): An Algorithm for Automatic Coding of Paralinguistic Nonverbal Parts of Speech in Text

Brands and consumers alike have become creators and distributors of digital words, thus generating increasing interest in insights to be gained from text-based content. This work develops an algorithm to identify textual paralanguage, defined as nonverbal parts of speech expressed in online communication. The authors develop and validate a paralanguage classifier (called PARA) using social media data from Twitter, YouTube, and Instagram (N = 1,241,489 posts). Using auditory, tactile, and visual properties of text, PARA detects nonverbal communication cues, aspects of text often neglected by other word-based sentiment lexica. This work is the first to reveal the importance of textual paralanguage as a critical indicator of sentiment valence and intensity. The authors further demonstrate that automatically detected textual paralanguage can predict consumer engagement above and beyond existing text analytics tools. The algorithm is designed for researchers, scholars, and practitioners seeking to optimize marketing communications and offers a methodological advancement to quantify the importance of not only what is said verbally but how it is said nonverbally.

Water Policy Evaluation Based on the Multi-Source Data-Driven Text Mining: A Case Study of the Strictest Water Resource Management Policy in China

The strictest water resources management (SWRM) policy is a critical policy to address China’s severe water shortage and pollution problems, and aims to promote sustainable water development and water governance. Based on data mining from multiple sources, including policy text from the strictest water resource management policy from 2011 to 2021, the reports of major media websites, and the Baidu Index, this study used the ROST-CM6 text-analysis tool to analyze the policy content, public opinion, and public perception of the strictest water resources management policy quantitatively and visually. The results found that the policy text and public-opinion are given high attention to the water resources assessment, water control management, and water resources protection, but the policy text focuses on the macro level, and pays more attention to national development and long-term planning. The public opinion belongs to the micro level and is more economic, and there is a certain degree of media bias. With notable regional disparities, the general public’s opinion of the harshest water resource management policy has been rising every year. This research adds to the global body of knowledge on water governance, and serves as a guide for Chinese and other governments looking to improve their water resource management strategies.