Word Frequency Research Articles

Development of somatotyping in Russia according to scientometric analysis from 2013 to 2023

BACKGROUND: Somatotyping has a long history and at the same time is widespread today in medicine, sports, and biomedical research, which necessitates the need to combat the inertia of scientific paradigms and methods .
 AIM: The goal is to identify the main trends and patterns in the development of modern somatotyping based on scientometrics of publication activity since 2013.
 METHODS: Based on RSCI data, a database was formed on the use of somatotyping for the period from 2013 to 2023. Key words of publications, frequency and scientific areas of application of somatotyping schemes were analyzed.
 RESULTS: Anthropometry techniques were most often used to identify predictors of adaptation failure or disease development. When searching for characteristics of samples that differ in influencing factors, body composition was more often used. An increase in studies using somatotyping according to Heath-Carter and Chernorutsky has been revealed. The share of studies performed using the methods of R.N. is decreasing. Dorokhov and V.G. Petrukhin and Taner, and the variety of somatotyping schemes used. In the study of adolescence, first adulthood and when somatotyping women, somatotyping according to R.N. was more often used. Dorokhov, in clinical studies - according to Z. Rees and H. Eisenck, in the study of athletes - according to Heath-Carter, in combined somatotyping - according to Bashkirov, Taner and Z. Rees and H. Eisenck.
 CONCLUSION: The main problem that anthropologists faced was the comparability of the results obtained using different somatotyping schemes.

Analysis of Damage Assessment Tweets During Disaster using Sentiment Analysis

This seems to be an abstract or summary of a paper on monitoring Twitter for damage assessments after a disaster. Using simple linear regression and Support Vector Regression methods for weighting and the random forest methodology for classification, the research provides a novel approach that makes use of low-level lexical characteristics, top-most frequency word features, and syntactic elements relevant to damage assessment. The accuracy of the suggested method for identifying damage assessment tweets is 94.62%, as measured across 14 typical disaster datasets for binary and multi-class categorization. Significant advancements were observed when comparing the proposed method to the state-of-the-art for both in-domain and cross-domain scenarios. The suggested method does not require labelled tweets or tweets of a specific disaster kind in order to be trained and implemented; instead, it can be trained on historical disaster datasets.

Topic Modelling of Management Research Assertions to Develop Insights into the Role of Artificial Intelligence in Enhancing the Value Propositions of Early-Stage Growth-Oriented Companies

The article suggests a Value Proposition (VP) framework that enables analysis of the beneficial impact of Artificial Intelligence (AI) resources and capabilities on specific VP activities. To develop such a framework, we examined existing business and management research publications to identify and extract assertions that could be used as a source of actionable insights for early-stage growth-oriented companies. The extracted assertions were assembled into a corpus of texts that was subjected to topic modelling analysis—a machine learning approach to natural language processing that is used to identify latent themes in large corpora of text documents. The topic modelling resulted in the identification of seven topics. Each topic is defined by a set of most frequent words co-occurring in a distinctive subset of texts that could be interpreted in terms of activities constituting the core elements of the VP framework. We then examined each activity in terms of its potential to be enhanced by employing AI resources and capabilities. The interpretation of the topic modelling results led to the identification of seven topics: (1) Value created; (2) Stakeholder value propositions; (3) Foreign market entry; (4) Customer base; (5) Continuous improvement; (6) Cross-border operations; and (7) Company image. The uniqueness of the adopted topic modelling approach consists in the quality of the assertions and the interpretation of the seven topics as an activity framework, i.e., in its capacity to generate actionable insights for practitioners. The additional analysis suggests that there is a potential for AI to enhance the emerging four core elements of the VP framework: Value created, Stakeholder value propositions, Foreign market entry, and Customer base. More importantly, we found that the greatest number of assertions related to activities that could be enhanced by AI are part of the Customer base topic, i.e., the topic that is most directly related to the growth potential of the companies. In addition, the VP framework suggests that a company’s customer base growth is continuously enhanced through a positive loop enabled by activities focused on the Continuous improvement of the activities and the amount of Value created, the alignment of Stakeholder value propositions, and companies’ Foreign market entry. Thus, the multiple-stakeholder perspective on VP development and foreign market entry appears as a factor that helps in understanding the beneficial impact of AI on the enhancement of the VP of early-stage growth-oriented companies.

Assessment of phonological awareness: Effects of emotional words on preschoolers’ skills

Phonological awareness is recognized as a precursor to reading success. Choosing material to assess children’s phonological awareness is a major challenge. The literature highlights factors that can influence children’s phonological skills, such as the frequency of words, their lexical status, and their linguistic parameters. To date, no study has looked at the potential effect of the emotional parameters of words on phonological skills. The emotional polarity of words (valence: pleasant, unpleasant) has effects on cognitive skills related to phonological awareness, i.e., executive functions, and working memory. The aim of this research is to measure the influence of emotional (positive, negative) and non-emotional (neutral, nonwords) words on the phonological awareness of children aged 4–6 years. Our results show a differential effect of emotional valence words on phonological skills. More specifically, the emotional valence of words facilitates the performance of epiphonological tasks, whereas negative valence disrupts the performance of metaphonological tasks. These results are discussed in relation to current models of interaction between emotion and cognition.

Exploring Spanish writing abilities of children with developmental language disorder in expository texts.

Numerous studies have shown that children with developmental language disorder (DLD), in addition to oral language difficulties, exhibit impaired writing abilities. Their texts contain problems in grammar, organization, cohesion, and length of written output. However, most of these studies have been conducted with English speakers. English is characterized by complex phonological structure, opaque orthography, poor morphology and strict word order. The aim of this research is to observe the writing abilities of children with DLD in a language with simple phonological structure, transparent orthography, rich morphology and flexible word order like Spanish in the production of expository texts. Twenty-six children with DLD (mean age in months = 128.85) and 26 age-and sex-matched typically developing (TD) children (mean age in months = 124.61) wrote an expository text about their favorite animal. In order to analyze how the two groups plan and encode written texts, we looked at word frequency and sentence structure, grammatical complexity and lexical density, and omissions and errors. Compared to the TD group, the children with DLD omitted more content words; made more errors with functional words, verb conjugation and inflectional morphemes, and made a large number of spelling errors. Moreover, they wrote fewer words, fewer sentences, and less structurally and lexically complex texts. These results show that children with DLD who speak a transparent orthography language such as Spanish also have difficulties in most language areas when producing written texts. Our findings should be considered when planning and designing interventions.

English Vocabulary Learning Aid System Using Digital Twin Wasserstein Generative Adversarial Network Optimized With Jelly Fish Optimization Algorithm

ABSTRACT Natural Language Processing (NLP) is a technology that permits computers to recognize human languages. Words are the fundamental unit of analysis in deep-level grammatical and semantic analysis. The main goal of NLP is typically word segmentation. Since the machine learning techniques cannot be directly applied to the practical issue of significant structural disparities between various data modalities in a multi-modal context. In this paper, English Vocabulary Learning Aid System Using Digital Twin Wasserstein Generative Adversarial Network Optimized using Jelly Fish Optimization Algorithm is proposed. The problematic of multiple modal data heterogeneity is handled by the feature extraction of Parameterized Local Maximum Synchro squeezing Transform and extract the features such as Phonetic features, sentence length, word embedding’s, part of speech tags, word frequencies, N-grams. Then, the Digital twin Wasserstein generative adversarial network classifies the English vocabulary to easy words, intermediate words, and difficult words. The performance of the proposed EVLS-DtwinWGAN-NLP approach attains 3.101%, 7.12%, 7.73% higher accuracy, 24.13%, 13.04%, 29.51% lower computation Time and 2.292%, 5.365%, 1.551% higher AUC compared with existing methods like Feature extraction and analysis of natural language processing for deep learning English language (EVLS-BiLSTM-NLP), State of art for semantic analysis of natural language processing (EVLS-SA-NLP) respectively.

When is a word in good company for learning?

Although identifying the referents of single words is often cited as a key challenge for getting word learning off the ground, it overlooks the fact that young learners consistently encounter words in the context of other words. How does this company help or hinder word learning? Prior investigations into early word learning from children's real-world language input have yielded conflicting results, with some influential findings suggesting an advantage for words that keep a diverse company of other words, and others suggesting the opposite. Here, we sought to triangulate the source of this conflict, comparing different measures of diversity and approaches to controlling for correlated effects of word frequency across multiple languages. The results were striking: while different diversity measures on their own yielded conflicting results, once nonlinear relationships with word frequency were controlled, we found convergent evidence that contextual consistency supports early word learning. RESEARCH HIGHLIGHTS: The words children learn occur in a sea of other words. The company words keep ranges from highly variable to highly consistent and circumscribed. Prior findings conflict over whether variability versus consistency helps early word learning. Accounting for correlated effects of word frequency resolved the conflict across multiple languages. Results reveal convergent evidence that consistency helps early word learning.

Variation in the 1sg.indef: More than you wanted to know

AbstractThe first person singular indefinite or non-definite of Hungarian verbs that end in -ik shows variation between the regular -k suffix and the -m suffix, used otherwise in the definite. This variation is systematic and subject to metalinguistic awareness. Our study relies on previous quantitative work, a frequency dictionary compiled from the new Hungarian Webcorpus, as well as a forced-choice elicitation experiment to assess the role of word frequency, word length, derivational endings, and across-form similarity in shaping this variation. We find that first person singular indefinite variation is largely defined by natural categories: verbs that look similar will also show a similar preference to -k/-m. This pattern is attested in the webcorpus as well as in participant responses in the elicitation task.

The Impact of Russian Troll Tweets: Analyzing Political Motivation in Tweets from the Internet Research Agency

This research investigates the impact of social media trolling on political events such as elections. The study utilizes the dataset of the Internet Research Agency, a Russian "troll factory" indicted by the US Justice Department in February 2018, to analyze tweets from 2012 to 2018, with the aim of creating a classification algorithm that will predict the political motivation of future tweets. The study involves running various classification algorithms, including Naive Bayes, K Nearest Neighbors, Random Forest, Histogram-based Gradient Boosting Classification Tree, and Light Gradient Boosting Machine. Data cleaning and categorization were done, assigning numeric values to each category. The resulting model scores and a breakdown of precision per category were analyzed. Word frequency analysis was also conducted to identify the most frequent words and understand the overall sentiment of the tweets and the topics targeted by the trolls. The research findings indicate that predicting the category of tweets is challenging, but the classification algorithms were able to do so with relatively good accuracy, especially for the NewsFeed and HashtagGamer categories. The study emphasizes the need for further research to improve accuracy of tweet classification algorithms and for social media platforms to continue their efforts to identify and remove troll accounts that aim to undermine political bias.

Exploring the connotation of “Local Community” in ICOMOS discourse: a textual analysis based on authoritative documents

As an authoritative institution in cultural heritage conservation, ICOMOS plays a crucial role in guiding local communities’ participation in heritage conservation. However, its scattered and vague descriptions of local communities pose significant obstacles to further research and practice of community participation in heritage conservation. Given the increasing importance of local communities in heritage conservation, it is essential to systematically explore the connotation of Local Community connotations within ICOMOS discourse. This research employs Natural Language Processing methods to analyze ICOMOS’s descriptions of Local Community. Utilizing computational techniques of word frequency calculation, LDA (Latent Dirichlet Allocation) topic model keyword calculation, and hierarchical clustering calculation, the research uncovers relevant keywords and its thematic clusters of Local Community. These findings are further elucidated by aligning them with the principles outlined in authoritative documents of ICOMOS. The research indicates that ICOMOS’s descriptions of Local Community can be summarized into four main keywords families. These keywords families can be summarized as a comprehensive Local Community “three-level, four-family” keywords system. The “Tourism” keywords family exhibits a close association with Local Community, highlighting ICOMOS’s heightened emphasis on heritage tourism. The “Management-Development” keywords family occupies the second level, emphasizing fundamental principles for local communities’ participation in heritage practices. The “Traditional-Knowledge” and “Social-Economic” Keywords Families, situated in the third level, respectively describe the value attributes and conservation approaches of local communities. Each keywords family formed over different periods, exhibiting varying trends of development. By systematically integrating ICOMOS’s descriptions of Local Community and employing Natural Language Processing for in-depth exploration, This research aims to construct a cognitive understanding of local communities from a new perspective of quantitative text analysis, with the intention of providing theoretical references for subsequent research on local communities.

The longitudinal relations between mental state talk and theory of mind

BackgroundPrevious investigations of associations between children’s Theory of Mind (ToM) and parents’ use of words relating to mental states (or mental state talk; MST) have predominantly been performed using cross-sectional designs and false belief tasks as indicators of ToM.MethodsWe here report a longitudinal study of 3–5 year-olds (n = 80) investigating ToM development using the ToM scale and three different parental MST types: the absolute frequency of words, the proportions of words, and the vocabulary size.ResultsOur results revealed significant relations between all parental MST types and later child ToM. Proportions of parental MST were most often related to the children’s ToM at 4 years of age. However, the rate at which the children developed ToM from 3 to 5 years of age was associated with the other two parental MST type measures, namely, absolute frequency and vocabulary size. Additionally, our analyses revealed that parents’ use of cognitive MST words (e.g., think, or know) were most frequently associated with children’s ToM at 4 years of age compared to emotion and desire-related MST words.ConclusionsWe conclude that the parental ability to capture the thoughts, beliefs, and knowledge present in different scenarios is associated with children’s ability to understand other minds. Moreover, parents’ way of talking about the mental states of others is associated with their children’s ability to understand and further develop ToM.

Parafoveal Word Frequency Does Not Modulate the Effect of Foveal Load on Preview in Chinese Reading: Evidence from Eye Movements.

The foveal load effect is one of the most fundamental effects in reading psychology, and also one of the most controversial issues in recent years. The foveal load effect refers to the phenomenon that the difficulty of foveal processing affects parafoveal preview. In Chinese reading, whether the foveal load effect exists, as well as whether this effect is modulated by parafoveal word frequency, remains unclear. In this study, the eye-tracking technique was used to track the eye movements of 48 subjects. Utilized the boundary paradigm with single-character words as parafoveal words, the present study manipulated foveal word frequency (high and low), parafoveal word frequency (high and low), and two types of preview (identical preview and pseudocharacter preview) to investigate these questions. The results revealed that the foveal word frequency does not influence preview, suggesting the absence of the foveal load effect when using single-character words as parafoveal words. Furthermore, parafoveal word frequency does not modulate the effect of the foveal load on the preview. This empirical evidence contributes to refining the understanding of the Chinese reading model.

Sentiment analysis using averaged weighted word vector features.

People use the World Wide Web heavily to share their experiences with entities such as products, services or travel destinations. Texts that provide online feedback through reviews and comments are essential for consumer decisions. These comments create a valuable source that may be used to measure satisfaction related to products or services. Sentiment analysis is the task of identifying opinions expressed in such text fragments. In this work, we develop two methods that combine different types of word vectors to learn and estimate the polarity of reviews. We create average review vectors from word vectors and add weights to these review vectors using word frequencies in positive and negative sensitivity-tagged reviews. We applied the methods to several datasets from different domains used as standard sentiment analysis benchmarks. We ensemble the techniques with each other and existing methods, and we compare them with the approaches in the literature. The results show that the performances of our approaches outperform the state-of-the-art success rates.

Does the Textual Tone of Analyst Reports Have Valuable Information? Korean Evidence*

AbstractWe investigate whether the text of analyst reports can provide additional information beyond the recommendation and target price. Positive and negative word lexicons are generated through an automated Bayesian learning method applied to Korean analyst reports spanning from 2016 to 2018. Then, the textual tone of an analyst report is quantified as the difference between the frequencies of positive and negative words in the text. The announcement returns of portfolios sorted by textual tone exhibit significant differences ranging from 1.14% to 2.82% within the same recommendation or target price revision group. Regression analysis also reveals significant association between the textual tone of analyst reports and stock announcement returns, even when controlling for the recommendation and target price. Notably, the text proves to be more informative in negative tones and within firms with limited analyst coverage. Our results indicate that textual analysis can unveil nuanced analyst opinions not captured by numerical information.

Memory modeling of counterfactual generation.

We use a computational model of memory search to study how people generate counterfactual outcomes in response to an established target outcome. Hierarchical Bayesian model fitting to data from six experiments reveals that counterfactual outcomes that are perceived as more desirable and more likely to occur are also more likely to come to mind and are generated earlier than other outcomes. Additionally, core memory mechanisms such as semantic clustering and word frequency biases have a strong influence on retrieval dynamics in counterfactual thinking. Finally, we find that the set of counterfactuals that come to mind can be manipulated by modifying the total number of counterfactuals that participants are prompted to generate, and our model can predict these effects. Overall, our findings demonstrate how computational memory search models can be integrated with current theories of counterfactual thinking to provide novel insights into the process of generating counterfactual thoughts. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

English tweets on allergy: Content analysis and association with surveillance data.

Analysis of X (formerly Twitter) posts can inform on the interest/perceptions that social media users have on health subjects. In this study, we aimed to analyse tweets on allergic conditions, comparing them with surveillance data. We retrieved tweets from England on "allergy," "asthma," and "allergic rhinitis," published between 2016 and 2021. We estimated the correlation between the frequency of tweets on "asthma" and "allergic rhinitis" and English surveillance data on the incidence of asthma and allergic rhinitis medical visits. We performed sentiment analysis, computing a score informing on the emotional tone of assessed tweets. We applied a topic modelling approach to identify topics (clusters of words frequently occurring together) for tweets on each assessed condition. We analysed a total of 13,605 tweets on "allergy," 7767 tweets on "asthma," and 11,974 tweets on "allergic rhinitis." Food-related words were preponderant on tweets on "allergy," while "eyes" was the most frequent meaningful word on "allergy rhinitis" tweets. We observed seasonal patterns for tweets on "allergic rhinitis," both in their frequency and sentiment - the incidence of allergic rhinitis medical visits was moderately to strongly correlated with the frequency (ρ = 0.866) and sentiment (ρ = -0.474) of tweets on "allergic rhinitis." For tweets on "asthma," no such patterns/correlations were observed. The average sentiment score was negative for all assessed conditions, ranging from -0.004 ("asthma") to -0.083 ("allergic rhinitis"). Tweets on "allergic rhinitis" displayed a seasonal pattern regarding their frequency and sentiment, which correlated with surveillance data. No such patterns were observed for "asthma."

Digital transformation, competitive strategy choices and firm value: evidence from China

PurposeThis research aims to examine the effects of corporate digital transformation on firm value, with a particular focus on the mediating roles played by cost leadership and differentiation strategies.Design/methodology/approachThis study employs word frequency analysis to create corporate digital transformation indicators and determine how corporate digital transformation impacts firm value. The data used in the analysis comes from 2,056 listed manufacturing enterprises in China between 2010 and 2019.FindingsThis study demonstrates that digital transformation has a favorable impact on firm value, and that cost leadership strategy and differentiation strategy significantly mediate the relationship between both of them.Research limitations/implicationsThis study utilized word frequency analysis to assess the state of corporate digital transformation. It lacked a more thorough description of internal production processes, operational efficiency, and the pace of digital transformation.Practical implicationsThe results of this study can not only promote the digital transformation and firm value, but also provide a theoretical basis for enterprises to choose a reasonable competitive strategy in the digital transformation.Originality/valueThis study contributes significantly to the field of firm value research by including digital transformation as a fundamental component. Furthermore, it investigates how cost leadership strategy and differentiation strategy play mediating roles, providing a new perspective and explanatory mechanism for understanding the influence of digital transformation on firm value.

Processing Mandarin Chinese Compound Words by Native Speakers and Second Language Learners: Word Frequency, Semantic Transparency, and Word Structure

This study examined how Chinese native speakers (NSs) and second language (L2) learners process compound words. The findings showed that they used the hybrid model of coexistence for whole word and morphemes; and were influenced by word frequency, semantic transparency, and word structure. The results revealed that two groups of participants used hybrid representation when identifying high-frequency words and whole-word representation when identifying low-frequency words. Besides, semantic transparency might impact word structure awareness, and subject-predicate words were the most difficult to process. The research also showed that L2 learners’ word frequency effect was more robust than NSs’; morpheme location information might affect NSs, but L2 learners could not process it. There was variation in NSs’ speed in recognizing transparent and obscure words, but there was no difference among L2 learners. Besides, L2 learners’ word recognition speed could not reach the levels of NSs.

Emotional context and predictability in naturalistic reading aloud.

[Correction Notice: An Erratum for this article was reported in Vol 24(3) of Emotion (see record 2024-72515-001). In the article (https://doi.org/10.1037/emo0001298), Table 1 as originally reported contained an error with respect to participant demographics. Specifically, participants who had selected multiple categories for race/ethnicity were mistakenly assigned to only the first alphabetical category selected. Updates have been made in the Race/ethnicity section of Table 1, to change the heading "American Indian or Alaska Native" to "Multiple selected," and to the relevant statistics under that heading as well as under the "Hispanic, Latino/a/x, or Spanish origin" and "Asian" headings. No inferential statistics are impacted by this correction, nor does it affect the results or conclusions of the article. All versions of this article have been corrected.] A robust experimental literature has found that word frequency and lexical valence contribute to visual word processing at the level of the individual word. Extensions of this literature to simplified sentences have essentially corroborated single-word findings, albeit with important influences of the unfolding discourse context, which may strengthen or attenuate single-word effects. This study sought to extend current knowledge one step further, beyond stand-alone sentences or sentence pairs, by investigating how word frequency and lexical valence, along with their interactions, influence oral reading performance for multisentence stimuli in a naturalistic context. Lexical features were averaged over short passages of text, which were presented to participants on-screen simultaneously, and performance was assessed as reading speed, in words per second. Overall, we find that the same patterns emerge for multisentence oral reading as in the prior literature: strong frequency effects that benefit higher-frequency content, a positivity bias that increases reading speed for more positive content, and an important interaction that disfavors relatively more negative (less positive), high-frequency content. We discuss these findings in light of possible interpretations based on associative connectivity in the mental lexicon, as well as oculomotor dynamics during naturalistic reading. Our data suggest that reading speed of multisentence texts is a viable alternative, and one that offers enhanced ecological validity, for investigations of visual word processing dynamics. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Written Language: A Promising Gateway to Anxiety Disorders Assessment

Currently, self-report measures are the primary assessment tool for anxiety disorders. Since they have some limitations, alternative measurements, such as language-based measures, are worth investigating. This paper explores which language markers signal anxiety in fictitious stories written in response to four Thematic Apperception Test (TAT) cards. Participants ( n = 492) from a non-probabilistic convenience sample were asked to write a short story next to each TAT card after completing the Generalized Anxiety Disorder-7. We used RoLIWC2015 to conduct the text analysis and applied the LASSO method to identify which language markers predict anxiety. The results showed that the respondents scoring high on anxiety also tend to use more words expressing negative emotions, and fewer words expressing positive emotions. Moreover, their language contained a higher frequency of words that implied semantic differentiation (i.e., but, else) and a lower frequency of words indicating leisure. In conclusion, this paper aims to shed new light on the multimethod assessment of anxiety, mainly focused on specific language signatures as reliable predictors of anxiety symptoms. Further research using more extensive text data is recommended to discover more linguistic markers and improve prediction accuracy.