Word Frequency Research Articles

Topics in the Haystack: Enhancing Topic Quality through Corpus Expansion

Abstract Extracting and identifying latent topics in large text corpora have gained increasing importance in Natural Language Processing (NLP). Most models, whether probabilistic models similar to Latent Dirichlet Allocation (LDA) or neural topic models, follow the same underlying approach of topic interpretability and topic extraction. We propose a method that incorporates a deeper understanding of both sentence and document themes, and goes beyond simply analyzing word frequencies in the data. Through simple corpus expansion, our model can detect latent topics that may include uncommon words or neologisms, as well as words not present in the documents themselves. Additionally, we propose several new evaluation metrics based on intruder words and similarity measures in the semantic space. We present correlation coefficients with human identification of intruder words and achieve near-human level results at the word-intrusion task. We demonstrate the competitive performance of our method with a large benchmark study, and achieve superior results compared with state-of-the-art topic modeling and document clustering models. The code is available at the following link: https://github.com/AnFreTh/STREAM.

Psychological adjustment and perception of patients’ relatives and healthcare providers regarding continuous deep sedation until death: A mixed-methods study.

12098 Background: France is currently in the midst of societal discussions concerning new legislation on active assistance in dying. Continuous Deep Sedation Until Death (CDSUD) is considered by some a “French exception” since 2016, but 8 years later, findings suggest that this procedure is not really clear to all, including patients, their relatives and their healthcare providers (HCP). The primary objective of this original research is to document the psychological adjustment of patients’ relatives and HCPs in the cancer context. Methods: APSY-SED (ID-RCB 2021-A03042-39) is a multicentric prospective, longitudinal and observational study, using a mixed-methods design. A lexicometric analysis of the semi-structured interviews was performed using Iramuteq software. A questionnaire survey was conducted, encompassing for relatives: anxiety and depressive symptoms (STAI-S, BDI-II); for HCPs: Professional Stress and Professional experience (Karasek questionnaire). We present here the main results at T1 (when CDSUD is implemented). Results: Main quantitative results are presented in Table I. Regarding qualitative findings, fifteen research interviews with relatives and 15 interviews with HCP were analyzed. Most frequent words associated with CDSUD were its “objectives”, its specific “temporality”, the mention of “euthanasia”, and “saying goodbye”. Confusion between CDSUD and euthanasia persists in relatives and HCPs. Conclusions: On average, relatives reported severe anxiety in the context of CDSUD. The qualitative findings regarding the main concerns shared by the relatives concerning the CDSUD temporality perceived as brutal, shed light on this anxiety. The second assessment time of this study, 6 months after death, including measures of depression, anxiety, intensity of grief reactions, will allow to specify the adjustement at mid-term. This is the first empirical study carried out in France on the adjustment of relatives and HCPs caring for patients with cancer for whom CDSUD is discussed. [Table: see text]

Event-related Potential Measures of Visual Word Processing in Monolingual and Bilingual Children and Adults: A Focus on Word Frequency Effects.

How does language background influence the neural correlates of visual word recognition in children? To address this question, we used an ERP lexical decision task to examine first-language (L1) and second-language (L2) visual word processing in monolingual and bilingual school-aged children and young adults (n = 123). In particular, we focused on the effects of word frequency (an index of lexical accessibility) on RTs and the N400 ERP component. Behaviorally, we found larger L1 versus L2 word frequency effects among bilingual children, driven by faster and more accurate responses to higher-frequency words (no other language or age group differences were observed). Neurophysiologically, we found larger L1 word frequency effects in bilinguals versus monolinguals (across both age groups), reflected in more negative ERP amplitudes to lower-frequency words. However, the bilingual groups processed L1 and L2 words similarly, despite lower levels of subjective and objective L2 proficiency. Taken together, our findings suggest that divided L1 experience (but not L2 experience) influences the neural correlates of visual word recognition across childhood and adulthood.

조직 내 세대 갈등과 시니어의 기대 역할

The aging workforce and resulting intergenerational conflict in workplaces present common challenges for many enterprises. This study aims to explore potential solutions to this issue by investigating what younger employees (referred to as “juniors”) expect from their older counterparts (“seniors”). In order to do so, the movie “The Intern,” which depicts an older intern navigating intergenerational conflicts at work, serves as the focal point of analysis. selected as a research subject, and reviews of the movie have been used as a research sample. Reviews of the movie, predominantly written by individuals in their twenties and thirties, constitute the primary data sample representing “juniors.” A total of 44,657 reviews were collected using multiple text-minig techniques to extract connotative rather than denotative meanings from the dataset. Following preprocessing, these reviews were splitted into 72,286 sentences. Leveraging sentiment analysis, word frequency analysis, and k-means clustering method, the study categorized sentences into six distinct clusters based on their thematic content. Finally, the technical support of Large Language Model (LLM) and Retrieval Augmented Generation (RAG) produces the main themes and their corresponding key topics within each cluster. The study identifies four main themes: “Senior Mentors,” “Seniors in the Workplace,” “Seniors in General,” and “Senior-related Social Issues.” These findings shed light on the social roles that juniors expect seniors to perform, such as providing wisdom, mentorship, and serving as role models. Building upon existing literature suggesting that enhanced communication can narrow generation gaps in the workplace, the study underscores the importance of effective communication topics. Futher empirical research is encouraged to validate and expand upon these findings, exploring their practical implications.

Effects of Subjective Type Frequency and Phonetic Structure on L2 Morphological Processing: A Constructional Perspective

Little research has been conducted to investigate the effects of subjective type frequency and phonetic structure on morphological processing. From the perspective of construction, this study carried out two experiments to examine whether these two factors have impacts on the processing of derivatives by Chinese EFL learners. In Experiment 1, the masked priming paradigm produced facilitation effect for the prefixed words rather than the suffixed ones. However, participants with higher subjective type frequency of the target words did not differ significantly from their counterparts in response times (RTs). In Experiment 2, words whose phonetic structure is consistent with the morphological structure took significantly longer RTs than the inconsistent group and the monomorphemic words in the unprimed lexical decision task. These findings demonstrated that subjective type frequency may be a necessary but not a sufficient condition for the generalization of morphological constructions, while phonetic structure can affect the perceptual salience of affix, thus playing an important part in morphological decomposition.

Exploring conceptualisations of vocational education in China: how the hierarchical education system mirrors social hierarchy

This research examines conceptualisations of Chinese vocational education. The parallel routes of academic and vocational education sort students into either strand with standardised exams. However, the status of academic and vocational work has been established through a culture of Confucianism. In order to examine current conceptualisations of vocational education, website-based commenting was used as an e-research method. Postings on the Chinese internet community Zhihu (知乎) were analysed for word frequency and user voice and examined through the lens of vocational system classification theory. It was found that Chinese socio-historical perspectives inform societal perceptions of education; that social divides reinforce academic/vocational educational divides, and that the stratified educational system of China further contributes to vocational education’s unsatisfying learning environment. Since addressing societal perspectives is particularly complicated, it is recommended that those who seek to reduce educational inequality work to improve the educational quality and student experience of vocational education.

Cross-cultural study of hotel practices as perceived by Chinese and North American travellers: A data mining analysis

This paper aims to unearth the different perception styles of Chinese and North American travellers from analytic versus holistic thinking perspectives. Python was utilized to gather online textual data from Chinese and North American travellers, while word frequency analysis, latent Dirichlet allocation (LDA) topic modelling analysis and content analysis were employed to elucidate the perception styles in a cross-cultural context. In general, North American travellers mainly leaned towards analytic thinking, whereas Chinese travellers showcased a blend of holistic and analytic thought processes. The topic of travel, leisure and accommodation showed both holistic and analytic thinking styles. The topics of nature and environment, front desk service, and travel routes and scenic spot areas mainly represented a holistic thinking style. The topics of convenience and facilities, breakfast, transportation, hotel theme and features, and decoration and amenities mainly suggested an analytic thinking style. Hotels should consider the different perception styles of Chinese and North American travellers to facilitate strategies accordingly and to maximize the experience of travellers from different cultural backgrounds.

The Study of Tourist Experience in Historical and Cultural Towns Based on Online Texts: A Case Study of Gongtan Ancient Town

This paper takes Gongtan Ancient Town of Chongqing as a case study, takes the online comment text of tourists on the websites of Ctrip and Dianping as data, analyzes the online text through content analysis, and uses ROST CM-6 software to conduct word frequency, emotion and semantic network analysis, aiming to deeply explore the tourist experience of Gongtan Ancient Town. The results show that: (1) Tourists' experience of Gongtan Ancient Town consists of five dimensions: tourist destination perception, sensory experience, physical and mental experience, tourist destination characteristic experience and service experience. (2) Natural scenery, local characteristics and service guarantee are the main tourist attractions of Gongtan Ancient Town.(3) Tourists' overall experience and perception of tourism in Gongtan Ancient Town tend to be positive emotions. Tourists' dissatisfaction mainly comes from heavy traffic jams during holidays, poor service attitude of staff and poor accommodation conditions. (4) The semantic network is divided into three levels: the core area is the tourists' objective cognition of the scenic spot name and location; The sub-core circle is the cultural cognition of the ancient town and the characteristic architecture and activity projects; The edge layer is the image characteristic cognition and tourism evaluation of the ancient town. In addition, some optimization suggestions are put forward for the existing problems of Gongtan Ancient Town. The research results not only provide a scientific basis for the tourism development and construction of Gongtan Ancient town, but also provide a reference for the optimization of tourist experience in other ancient towns.

Measuring Phonological Complexity Using the Perfect Rhymes Dictionary (PeRDict)

This paper examines links between perfect rhymes and text readability and decoding using a measure of English rhymes called the Perfect Rhymes Dictionary (PeRDict). PeRDict is based on the Carnegie Mellon University Pronouncing Dictionary (the CMUdict) and provides rhyme counts for ∼48,000 words in English and for the most frequent 1,000, 2,500, 5,000, and 10,000 rhymes within the dictionary as measured by the Corpus of American English (COCA). Two assessments of PeRDICT are presented. The first examines the strength of rhyme features to predict text readability in conjunction with word neighborhood density effects reported by the English Lexicon Project (ELP) and a word frequency measure. The second examines the strength of rhyme features to predict word decoding in conjunction with word neighborhood effects and a word frequency measure. In both assessments, the number of rhymes per word was predictive of text or word processing beyond features related to word neighborhood effects and word frequency. Word rhyme counts performed more strongly in predicting text processing versus word processing.

Math Talk to Infants During Everyday Home Activities: Contextual Cues to Words About Number, Space, and Magnitude

ABSTRACT Infants begin to produce abstract “math” words – such as numbers (e.g., “two”), spatial terms (e.g., “down”), and magnitude words (e.g., “more”) – during their second postnatal year. Math words, as all words, are likely learned in the home setting during interactions with caregivers. However, everyday early exposure to math talk is understudied. What referents and social cues accompany caregivers’ math-related speech to infants? We examined U.S. infants’ (25 girls, 25 boys; ages 13 to 23 months; 60% white non-Latine) exposure to math words at home during everyday activities with their mothers. We documented three types of math words/phrases (i.e., number, spatial, and magnitude words), their referents (e.g., images in books), and the social cues that accompanied math talk (e.g., gestures). Math words appeared in 15% of mothers’ utterances (13,000+ math utterances in aggregate), increased with child age, mostly specified spatial concepts (e.g., location and direction), and tended to refer to objects and actions in the environment that were relevant to the child, such as toys, books, and the child’s body and behaviors. Social cues accompanied mothers’ math talk about half the time. Notably, certain types of referents and social cues aligned with specific types of math talk (e.g., manual gestures such as points accompanied talk about spatial features such as shapes). In the everyday home environment, infants experience frequent and varied math words, across a variety of activities, in the presence of salient social and contextual cues.

Political Discourse Analysis of Donald Trump’s Rhetoric: A Linguistic Study of Cognition and Discursivity

To elucidate the deliberate use of Islamophobia in the political sphere, this study carefully examines Donald Trump's presidential campaign speeches using political discourse analysis (PDA). Trump’s Islamophobic position in political discourse has sparked a global discussion. His divisive political language during the 2016 elections contributed to an overall rise in incidents showing hatred for Muslims in the America by painting a poor picture of the Muslim world. The present study employs the theoretical framework of Van Dijk (1998) to examine the socio-political contexts of the discourse: participants’ insight (their goals, relevant knowledge and their belief system), group organization, power dynamics, as well as favourable and unfavourable perceptions of “us” against “them”. The purpose of the work is to highlight the processes by which Islamophobia is created, propagated, and normalised in public discourse. The nature of the current investigation involves heterogeneous techniques. It analyzes the text using PDA methods. Additionally, it computes word frequency to determine the proportion of positive to negative terms in Donald Trump's political speech. The analysis reveals that political leaders use language as a tool to serve their own political ends. This study focusses on understanding the complex interplay between language, power, and ideology in contemporary political communication. By shedding light on the pervasive influence of Islamophobia as a political strategy, it underscores the imperative of critically engaging with political discourse to challenge hegemonic narratives, and to foster all-inclusive and democratic societies. Political leaders frequently employ language as a weapon to further their agendas. This research work highlights the necessity of critically interacting with political discourse to challenge dominant narratives and promote inclusive and democratic societies by bringing to light the pervasive influence of Islamophobia as a political strategy.

Building a Value-Based Workforce Through Islamic Work Value-Based Recruitment: A Conceptual Review

The increasing ethical infringements, moral violations, and disciplinary problems among employees point to the deteriorations in employee values. Hiring employees merely on common competency-based recruitment (CBR) versus value-based recruitment (VBR) ignores the critical role of values in guiding employee behaviours, thus contributing further to the problems. This study involves a qualitative approach using NVivo text analysis tool to analyse texts and word frequency from 44 references, including 35 journal articles, six policy papers, two books or book chapters and one conference proceedings. The results show that Public Sector ICT Management Review was the top journal with the highest coverage of the study’s main themes, followed by the Journal of Education for Teaching, the Human Resource Management Review, and the Advances in Health Science Education indicating the importance of the value-based workforce in these four areas of employment (i.e., public sector, education, human resource, and health). The word frequency analysis also shows the five most-used words were “values,” “work,” “based,” “selection,” and “recruitment.” These findings show that the references used in this study are valid and reliable relevant to the study’s main themes. Value-based workforce relates to value-based recruitment – building a value-based workforce begins with a good recruitment strategy. In the Islamic context, Islamic work values are highly influential and Islamic work-related values have been used as recruitment conditions for hiring employees during the Prophet Muhammad’s (pbuh) time and the rightly guided caliphs. If incorporated into the value- based model of recruitment, Islamic-related work values could potentially build a value- based workforce. This study has a significant implication to managers especially in finding good employees for the success of the organisation. Recruiting employees through value-based recruitment and selection is believed could reduce many work-related issues and improve organisational effectiveness. This study adds to the body of knowledge on value- based hiring by incorporating theories and Islamic work ideals, which can be used as a guide for future research.

Development of optimized cascaded LSTM with Seq2seqNet and transformer net for aspect-based sentiment analysis framework

The recent development of communication technologies made it possible for people to share opinions on various social media platforms. The opinion of the people is converted into small-sized textual data. Aspect Based Sentiment Analysis (ABSA) is a process used by businesses and other organizations to assess these textual data in order to comprehend people’s opinions about the services or products offered by them. The majority of earlier Sentiment Analysis (SA) research uses lexicons, word frequencies, or black box techniques to obtain the sentiment in the text. It should be highlighted that these methods disregard the relationships and interdependence between words in terms of semantics. Hence, an efficient ABSA framework to determine the sentiment from the textual reviews of the customers is developed in this work. Initially, the raw text review data is collected from the standard benchmark datasets. The gathered text reviews undergo text pre-processing to neglect the unwanted words and characters from the input text document. The pre-processed data is directly provided to the feature extraction phase in which the seq2seq network and transformer network are employed. Further, the optimal features from the two resultant features are chosen by utilizing the proposed Modified Bird Swarm-Ladybug Beetle Optimization (MBS-LBO). After obtaining optimal features, these features are fused together and given to the final detection model. Consequently, the Optimized Cascaded Long Short Term Memory (OCas-LSTM) is proposed for predicting the sentiments from the given review by the users. Here, the parameters are tuned optimally by the MBS-LBO algorithm, and also it is utilized for enhancing the performance rate. The experimental evaluation is made to reveal the excellent performance of the developed SA model by contrasting it with conventional models.

Russian elision as lenition to zero

While there has been extensive documentation of elision in colloquial Russian speech, there is minimal phonetic research on its underlying causes. The present study performs an exploratory acoustic analysis on spontaneous Russian speech and ties its phonetic correlates to previously described lenition processes by demonstrating their continuous nature. Special attention is given to /v/ due to its sonorant-like qualities in Russian. Furthermore, the study uses the results of this analysis to propose a framework for predicting elided forms using both language-general processes alongside word frequency.

Lexical-semantic processing in preschoolers with Developmental Language Disorder: an eye tracking study.

This study examined lexical-semantic processing in children with Developmental Language Disorder (DLD) during visually situated comprehension of real-time spoken words. Existing evidence suggests that children with DLD may experience challenges in lexical access and retrieval, as well as greater lexical competition compared to their peers with Typical Development (TD). However, the specific nature of these difficulties remains unclear. Using eye-tracking methodology, the study investigated the real-time comprehension of semantic relationships in children with DLD and their age-matched peers. The results revealed that, for relatively frequent nouns, both groups demonstrated similar comprehension of semantic relationships. Both groups favored the semantic competitor when it appeared with an unrelated visual referent. In turn, when the semantic competitor appeared with the visual referent of the spoken word, both groups disregarded the competitor. This finding shows that, although children with DLD usually present a relatively impoverished vocabulary, frequent nouns may not pose greater difficulties for them. While the temporal course of preference for the competitor or the referent was similar between the two groups, numerical, though non-significant, differences in the extension of the clusters were observed. In summary, this research demonstrates that monolingual preschoolers with DLD exhibit similar lexical access to frequent words compared to their peers with TD. Future studies should investigate the performance of children with DLD on less frequent words to provide a comprehensive understanding of their lexical-semantic abilities.

ESG performance and financial distress prediction of energy enterprises

In the current drive to cut global carbon emissions, energy companies are facing intensifying policy pressures. This study investigates the impact of ESG (Environmental, Social, Governance) performance on the risk of corporate financial distress in the energy sector. Using a tripartite methodology of sentiment, topic, and word frequency analysis, we measure the characteristics of texts of ESG reports. These ESG-related textual variables, combined with company carbon performance and other variables, are integrated into the CatBoost algorithm to predict financial distress. The empirical findings indicate that text words, topics and sentiments derived from ESG reports prove to be effective in forecasting financial distress in energy companies. Additionally, the CatBoost used in this study surpasses other models such as logistic regression and decision trees in predictive capability. This study demonstrates how incorporating textual analysis of ESG reports enhances the predictive accuracy for financial distress in energy companies, highlighting the important role of comprehensive ESG evaluation in financial risk assessment.

Harnessing Text and Web Analytics to Enhance Decision-Making in Job Opportunity Categorization

Text analytics is defined as a method of analyzing compilations of structured text such as dates, times, locations, semi structured text, such as HTML and JSON as well as unstructured text, such as word documents, videos, and images, to extract and discover trends and relationships without requiring the exact words or terms to convey those concepts. Web analytics on the other hand is the technology that collects, measures, analyses, and provides reports of data on how users use websites and web applications. It is used to track a number of aspects of direct user-website interactions, such as the number of visits, time spent on the site, and click pathway. It also aids in the identification of user interest areas and the enhancement of web application features. We used clustering techniques to categorize the job opportunities that are available for the job seekers. By implementing text analytics, text data may be grouped with the goal of providing outcomes in the form of word frequency distribution, pattern identification, and predictive analytics. Text analytics may create one-of-a-kind values to use in the improvement of decision-making and business processes, as well as the development of new business models.

Managing the E-commerce Data Deluge through Text Analytics and Web Management (Overview of Amazon.com)

Today, more than 80% of the big data handled in the e-commerce industry is text and unstructured data. Text analytics is an automated process for analyzing text and extracting useful information from it. It can discover trends and relationships in data. Web analytics is the collection, processing, and analysis of data in order to draw conclusions to optimize usability on a website. Web analytics can be used to improve the usability of a site by analyzing user behavior patterns such as time spent on the site, abandonment rates, most frequently accessed products, click-through rates, etc. It can also help analyze the interests of different user demographics, as it tracks granular details such as user demographics, age and gender, geography, and devices used as data. In order to obtain UpToDate information, the business can utilize business intelligence for real-time data processing, then they can practice stream analysis to analyse continuous flow of data. For instance, the business can collect instant information in Twitter or other social media and analyse it by using social media analysis. For website management, business can practice web analysis to analyse the customer’s behaviours. Tracking the customer’s activity, page view and conversion rate is important for business to analyse how to improve the website performance. Text analytics of comments received on Amazon can be used to group text data and produce results in terms of word frequency distribution and sentiment analysis. Text analytics could be used for decision making, improving service quality, and developing new business models.

Examining the Impact of Reading Fluency on Lexical Decision Results in French 6th Graders.

How does lexical decision behavior vary in students with the same grade level (all students were in their first year of middle-school), but different levels of reading fluency? Here, we tested a prediction of the dual-route model: as fluency increases, variations in the results may reflect a decreasing reliance on decoding and an increasing reliance on the lexical route. 1,501 French 6th graders passed a one-minute speeded reading-aloud task evaluating fluency, and a ten-minute computerized lexical decision task evaluating the impact of lexicality, length, word frequency and pseudoword type. As predicted, the word length effect varied dramatically with reading fluency, with the least fluent students showing a length effect even for frequent words. The frequency effect also varied, but solely in proportion to overall reading speed, suggesting that frequency affects the decision stage similarly in all readers, while length disproportionately impacts poor readers. Response times and errors were also affected by pseudoword type (e.g., letter substitutions or transpositions), but these effects showed minimal variation with fluency. Overall, lexical decision variables were excellent predictors of reading fluency (r = 0.62). Our results highlight the variability in middle-school reading ability and describe how a simple lexical decision task can be used to assess students' mental lexicon (vocabulary) and the automatization of reading skills.

“Because the News is Depressing as Hell”: Journalists’ Explanations of News Avoidance

What journalists believe about the growing phenomenon of news avoidance will affect the way they address it. In this paper we use an iterative, mixed-methods approach to analyze responses to an open-ended survey question which asked US journalists why they believe people consume little to no news. Our data analysis consisted of 3-phases: an inductive close reading to detect initial patterns, a quantification of patterns through a word frequency analysis, and a final close reading. We find that journalists’ responses about why people avoid news fit into three main categories based on whom or what they identified as responsible for this behavior. Some of these sources of blame were much more common than others, and they were associated with different tones, ranging from condescending to sympathetic. The most common reasons supplied were those blaming news itself (e.g., news is negative or biased), followed by those blaming contextual factors (e.g., life circumstances or competition from social media), and those blaming news avoiders themselves. In the discussion, we compare journalists’ beliefs about the causes of news avoidance with findings from research on news avoiders themselves and explore implications for possible efforts to address news avoidance in the future.