Keyword Relevance Research Articles

A Scientometrics Analysis of Global Research on Social Media: Trends, Influential Works, And Future Directions Explored

This research aims to study regulations on the use of social media around the world. This study uses a qualitative scientometric approach. The data source for this study was taken from the Scopus Database with the keywords "Social Media Use" and "Policies." There were 173 documents analyzed, and the data was filtered using bibliometrics based on keyword relevance, author country, and year of publication, which was limited to 2018-2022. Citespace software was used to process the data stored in RIS format. The study found that, firstly, 173 publications were addressing social media use and policy from 2018 to 2022. Second, the most published publications in the United States are 73; third, ten authors have collaborated in research on social media use and policy. The study also found that the social media use and policies of governments and countries worldwide continue to evolve as technology advances and people's behavior changes. Governments should make appropriate regulations and make good use of social media to communicate with the public and achieve their goals.

Resume Optimization Model Using Machine Learning Techniques

In the contemporary job market, where competition is fierce and employers are inundated with an ever-growing pool of resumes, the need for effective resume optimization has become paramount. Resumes serve as the first point of contact between job seekers and potential employers, playing a pivotal role in shaping initial perceptions. However, the traditional approach to resume crafting often lacks a systematic and data-driven methodology. A well-crafted resume plays a crucial role in securing employment opportunities. However, crafting an effective resume that resonates with both human recruiters and Applicant Tracking Systems (ATS) can be a daunting task. By employing natural language processing (NLP) and machine learning algorithms Multinomidal Naïve Bayes (MNB) and K Nearest Neighbour (KNN), this system extracts relevant features from resumes, such as keyword relevance, formatting styles, content organization, and overall readability. Through supervised learning models trained on a diverse dataset of resumes, the system can predict the effectiveness of a resume and generate actionable insights. Overall, the KNN model demonstrated effectiveness in automating the resume screening process, of 87% accuracy. The developed system not only provides accurate predictions but also offers interpretable explanations, enabling users to understand the factors contributing to the model's decisions. The system has the potential to benefit both job seekers and employers by facilitating better matches between candidates' qualifications and job requirements.

Markov Chains for User Queries Mining: Image Retrieval Application using Internet

Abstract: We suggest a brand-new technique for automatically annotating, indexing, and retrieving photos based on annotations. We introduce the novel technique, which we term Markovian Semantic Indexing (MSI), within the framework of an image retrieval system in online mode. In the event that such a system exists, the queries entered by the users are utilised to build an Aggregate Markov Chain (AMC), which defines the significance between the terms the system sees. The photographs are automatically annotated based on the queries entered by the users. Next, depending on the annotation of each image and the keyword relevance recorded in the AMC, a stochastic distance between them is presented. The suggested distance is given geometric meanings, and its relationship to a grouping in the keyword space is examined. The optimality qualities of the suggested distance are demonstrated by the use of a novel Markovian state similarity measure, the mean first cross passage time (CPT). Images are represented as points in a vector space, and MSI is used to calculate how similar two images are. In Annotation-Based Image Retrieval (ABIR) tasks, it is demonstrated that the novel method outperforms Latent Semantic Indexing (LSI) and probabilistic Latent Semantic Indexing (pLSI) methods in terms of Precision against Recall and has some theoretical advantages.

RICo: Reddit ideological communities

The main objective of our research is to gain a comprehensive understanding of the relationship between language usage within different communities and delineating the ideological narratives. We focus specifically on utilizing Natural Language Processing techniques to identify underlying narratives in the coded or suggestive language employed by non-normative communities associated with targeted violence. Earlier studies addressed the detection of ideological affiliation through surveys, user studies, and a limited number based on the content of text articles, which still require label curation. Previous work addressed label curation by using ideological subreddits (r/Liberal and r/Conservative for Liberal and Conservative classes) to label the articles shared on those subreddits according to their prescribed ideologies, albeit with a limited dataset.Building upon previous work, we use subreddit ideologies to categorize shared articles. In addition to the conservative and liberal classes, we introduce a new category called “Restricted” which encompasses text articles shared in subreddits that are restricted, privatized, or banned, such as r/TheDonald. The “Restricted” class encompasses posts tied to violence, regardless of conservative or liberal affiliations. Additionally, we augment our dataset with text articles from self-identified subreddits like r/progressive and r/askaconservative for the liberal and conservative classes, respectively. This results in an expanded dataset of 377,144 text articles, consisting of 72,488 liberal, 79,573 conservative, and 225,083 restricted class articles. Our goal is to analyze language variances in different ideological communities, investigate keyword relevance in labeling article orientations, especially in unseen cases (922,522 text articles), and delve into radicalized communities, conducting thorough analysis and interpretation of the results.

Evaluation of Indonesia’s police public service platforms through sentiment and thematic analysis

The Indonesian national police (Polri) offer public services through mobile apps: Digital korlantas polri (DigiKorlantas) and samsat digital nasional (SIGNAL). Sentiment analysis gauges public perceptions, serving as a basis for e-government evaluation using user ratings and comments from app stores. Keyword relevance is assessed via feature extraction and Naïve Bayes classification. Thematic analysis is implemented using N-grams methods to identify the factors affecting the effectiveness based on user experiences. The accuracy of the model reaches 81.09% where it indicates a high performance. DigiKorlantas acquires slightly more negative reviews in comparation with positive reviews which are 51% and 49% respectively. In contrast, positive sentiment is dominant on SIGNAL which reach 58%, compared with negative sentiment that in 42%. N-grams reveal similar review patterns for both apps. Some of the solutions are Korlantas Polri should enhance the verification functionality with several techniques such as retinex algorithms or optical character recognition pipeline and increase the capacity of supporting server then releasing an updated version of application to address errors or bugs. This analysis can be alternative evaluation by the Polri to measure the success of the application and find out the continuous improvement of the process and the system.

DINAMIKA PENELITIAN KEPATUHAN WAJIB PAJAK

This study explores the compliance of taxpayers in Indonesia from 2013 to 2023 by mapping research published in national scientific journals. The research methodology involves a thorough literature review, examining articles with the keyword "taxpayer compliance" on scientific journal websites. The population consists of articles on taxpayer compliance, and the sample is selected based on criteria such as keyword relevance, completeness, clarity, and online accessibilityThe mapping of journal names aims to categorize articles, facilitating information on journal consistency in publishing content related to taxpayer compliance. The findings reveal continuous publications on taxpayer compliance in scientific journals throughout the years, with 2017 dominating the articles. E-Jurnal Akuntansi emerges as the most prolific journal in publishing these articles. The prevalent research method is quantitative, with awareness of taxpayers being the most commonly studied variable. Implications of the study include recognizing E-Jurnal Akuntansi and Jurnal Akuntansi as promising channels for future researchers and the potential for more varied and in-depth results through the use of qualitative methods. The study also highlights the predominance of primary data sources and suggests the exploration of alternative sources such as observation and interviews for richer and more realistic data.

Using keywords in the automatic classification of language of gender violence

This paper employs lexical analysis tools, quantitative processing methods, and natural language processing procedures to analyze language samples and identify lexical items that support automatic topic detection in natural language processing. This paper discusses how keyword extraction, a technique from corpus linguistics, can be employed in obtaining features that improve automatic classification; in particular, this research is concerned with extracting keywords from a corpus obtained from social networks. The corpus consists of 1,841,385 words and is subdivided into three sub-corpora that have been categorized according to the topic of the comments in each one of them. These three topics are violence against women, violence against the LGBT community, and violence in general. The corpus has been obtained by scraping comments from YouTube videos that address issues such as street harassment, femicide, feminist movements, drug trafficking, forced disappearances, equal marriage, among others. The topic detection tasks performed with the corpus extracted from the social media showed that the keywords rendered a 98% accuracy when classifying the collection of comments from 51 videos, as one of the three categories mentioned above, and 92% when classifying almost 7,500 comments individually. When keywords were removed from the classification task and all words were used to perform the classification task, accuracy dropped by an average of 17%. These results support the argument for keyword relevance in automatic topic detection.

‘The monster in the back seat’

People living with dementia (PWDs) often experience stigma that is reinforced by negative representations of the condition in discourse. This paper addresses the lack of research into this stigma by analyzing the representation of the condition in naturally occurring texts of people in direct contact with PWDs. Two blogs from Flemish family caregivers and one blog from a Flemish health professional were selected based on word count and keyword relevance, and studied both quantitatively and qualitatively. For the quantitative analysis keywords, n-grams, and collocations were identified in 225 blog posts, using Sketch Engine. For the qualitative analysis, 26 blog posts were analyzed by categorizing conceptual metaphors according to their conceptual mappings. The quantitative analysis indicates that the health professional employs a different approach in reporting, compared to the family caregivers. The qualitative analysis results in five conceptual mappings: PWDs as living dead, PWDs as trees, dementia as concealment, dementia as an acting entity and dementia as an attack. The frequency of these mappings shows that the condition is being dissociated from the PWDs, and described with more negative metaphors than the PWDs themselves.

A critical assessment of consumer reviews: A hybrid NLP-based methodology

Online reviews are integral to consumer decision-making while purchasing products on an e-commerce platform. Extant literature has conclusively established the effects of various review and reviewer related predictors towards perceived helpfulness. However, background research is limited in addressing the following problem: how can readers interpret the topical summary of many helpful reviews that explain multiple themes and consecutively focus in-depth? To fill this gap, we drew upon Shannon's Entropy Theory and Dual Process Theory to propose a set of predictors using NLP and text mining to examine helpfulness. We created four predictors - review depth, review divergence, semantic entropy and keyword relevance to build our primary empirical models. We also reported interesting findings from the interaction effects of the reviewer's credibility, age of review, and review divergence. We also validated the robustness of our results across different product categories and higher thresholds of helpfulness votes. Our study contributes to the electronic commerce literature with relevant managerial and theoretical implications through these findings.

Visual analytics and information extraction of geological content for text-based mineral exploration reports

A large amount of continuously increasing textual geoscience data is stored and not fully utilized. Text mining enables the discovery and analysis of valuable information,and presents valuable insights hidden in geological texts. This research aims to use text mining and visualization techniques to obtain content words -for the purpose of visually analyzing geological reports. The framework proposed in this study can enable researchers to quickly understand key information and improve the transmission efficiency of geological reports. First, we implemented an improved keyword extraction algorithm comprising the term frequency-inverse document frequency and word length to improve the accuracy of geological keyword extraction. Second, we extracted and visualized the relative importance as well as the links between content words that can represent the key information of geoscience reports using word-level information analysis and multidimensional scaling analysis. Finally, the keyword relevance and mutual clustering relations were visualized through graphs to provide an intuitive representation of the current state of the reports.

The moderating effect of influencer on the causal map of mutual information, coproducer and customer value: a thematic analysis of messages posted by brand communities

The COVID-19 pandemic forced customer behaviour to shift from partial online searching and buying to full use of online services. Most of the products shipped must be assembled or adjusted for personal consumption. With different needs, customers learn to customize their consumption into an experience that is more trustworthy and practical than using an influencer. This research aimed to investigate the effect of an influencer moderating the path between the coproducer and brand’s mutual information. To fulfil this objective, we collected Thai customers’ message posts from 50 brands’ fan pages in 2020. The data preparation process used word segmentation and keyword relevance identification. Finally, the data set of 300 vectors with four measurement variables, including coproducer, influencer, mutual information, and customer value, was analysed by PROCESS Model 7 in SPSS. The results showed that the antecedent variables have positive effects on customer value, with the exception of the influencer moderating variable, which has a negative coefficient of − 0.055 (p < 0.1). Since the moderating effect of influencers has decreased the indirect effect on customer value, traditional influencer marketing must be improved to support customers who engage in expertise consumption as coproducers.

Physician gender as a source of implicit bias affecting clinical decision-making processes: a scoping review

BackgroundThe demographic profile of practicing physicians is changing as more female medical students are graduating and practicing in the field. While the education received may not differ by gender, studies have shown that physician practice outcomes vary by provider gender. Various factors could contribute to these differences, including culture and explicit biases which may lead to implicit bias. This study aims to identify the available evidence of gender-based implicit bias throughout the delivery process of medicine.MethodsThis scoping review evaluated published literature within the PubMed, Ovid MEDLINE, PsychINFO, Web of Science, and BioMed Central databases pertaining to physician’s gender as a factor in the delivery of medicine. Arksey and O’Malley’s six-stage methodology was used as a framework and reported using the updated Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). Searches occurred between May 2020 and June 2020, and the timeframe was not limited. Included articles had gender as a factor in the delivery of medicine and implicit bias. Articles were excluded if they did not include the gender of the physician. After screening by reviewers and a medical librarian, study characteristics were charted and analyzed.ResultsThe initial search resulted in 2420 records. After screening, 162 of the records were selected based on title and keyword relevance. After additional screening, 15 records were ultimately included in the review based on full-text evaluation. Records were organized into sub-topics post hoc focused on clinical qualities, diagnostics, treatment, and outcomes.ConclusionThis scoping review found that gender-based implicit bias may be inadvertently acquired from culture and education. Although implicit bias is highly researched, much of the current literature focuses on the gender of the patient. This study found important gaps in the available literature regarding race and gender of the physician. Further studies could explore outcome differences between recent graduates and career physicians, for both female and male physicians.

The impact of software company size and culture on commercial real estate location and design

PurposeThis paper aims to investigate the relationship of software company culture and core values and project management methodologies on the demand for corporate real estate (CRE), impacting decisions regarding location, square footage, office design and amenities.Design/methodology/approachA researcher-administered survey was designed with the assistance of a purposive sample of brokers, architects and interior designers to elicit responses from the CRE officers in software companies at four stages of growth, from small, entrepreneurial startups to large, publicly traded software companies, located in the same metropolitan area of the USA. Quantitative responses are summarized with traditional statistics and data visualizations. Linguistic analysis, including sentiment analysis and keyword relevance, was performed on the unstructured, English text responses.FindingsDifferences exist in the office layouts, amenities and locations across the four software company size categories studied. Linguistic analysis of company descriptions of office design, culture and core values, and the relationship between the two, provide another way for brokers, investors and other stakeholders to understand company perspectives and communication idioms related to CRE needs. The research was unable to show any differences in any dependent variable based on software project management methodologies due to sampling limitations.Research limitations/implicationsThis study is limited by the sample size of the participating software companies based on access to company leadership. Results are not generalizable.Practical implicationsArchitects, investors, brokers and lenders may find value in using this study’s approach to better understand the needs of software technology clients. Specifically, stakeholders may find value in examining the linkage from software company size, culture and core values to CRE office layout, amenities and location.Originality/valueThe qualitative findings suggest that software company culture and core values and company size influence the design of the CRE demanded by software companies. Multivariate data visualization was designed to communicate longitudinal CRE data. Linguistic analysis was used to extract the emotional content and relevance scores from company descriptions of office design, company culture and core values and the reported effect of culture and core values on office design. Findings may be beneficial for stakeholders involved in the design, location and future CRE investments, and they suggest the need for future research on a larger sample.

A Text-Mining Framework for Supporting Systematic Reviews.

Systematic reviews (SRs) involve the identification, appraisal, and synthesis of all relevant studies for focused questions in a structured reproducible manner. High-quality SRs follow strict procedures and require significant resources and time. We investigated advanced text-mining approaches to reduce the burden associated with abstract screening in SRs and provide high-level information summary. A text-mining SR supporting framework consisting of three self-defined semantics-based ranking metrics was proposed, including keyword relevance, indexed-term relevance and topic relevance. Keyword relevance is based on the user-defined keyword list used in the search strategy. Indexed-term relevance is derived from indexed vocabulary developed by domain experts used for indexing journal articles and books. Topic relevance is defined as the semantic similarity among retrieved abstracts in terms of topics generated by latent Dirichlet allocation, a Bayesian-based model for discovering topics. We tested the proposed framework using three published SRs addressing a variety of topics (Mass Media Interventions, Rectal Cancer and Influenza Vaccine). The results showed that when 91.8%, 85.7%, and 49.3% of the abstract screening labor was saved, the recalls were as high as 100% for the three cases; respectively. Relevant studies identified manually showed strong topic similarity through topic analysis, which supported the inclusion of topic analysis as relevance metric. It was demonstrated that advanced text mining approaches can significantly reduce the abstract screening labor of SRs and provide an informative summary of relevant studies.

Image recommendation based on keyword relevance using absorbing Markov chain and image features

Image recommendation is an important feature of search engine, as tremendous amount of images are available online. It is necessary to retrieve relevant images to meet the user’s requirement. In this paper, we present an algorithm image recommendation with absorbing Markov chain (IRAbMC) to retrieve relevant images for a user’s input query. Images are ranked by calculating keyword relevance probability between annotated keywords from log and keywords of user input query. Keyword relevance is computed using absorbing Markov chain. Images are reranked using image visual features. Experimental results show that the IRAbMC algorithm outperforms Markovian semantic indexing (MSI) method with improved relevance score of retrieved ranked images.

빅데이터 환경에서 기계학습 알고리즘 응용을 통한 보안 성향 분석 기법

Abstract Recently, with the activation of the industry related to the big data, the global security companies have expanded their scopes from structured to unstructured data for the intelligent security threat monitoring and prevention, and they show the trend to utilize the technique of user's tendency analysis for security prevention. This is because the information scope that can be deducted from the existing structured data(Quantify existing available data) analysis is limited. This study is to utilize the analysis of security tendency(Items classified purpose distinction, positive, negative judgment, key analysis of keyword relevance) applying the machine learning algorithm(Naive Bayes, Decision Tree, K-nearest neighbor, Apriori) in the big data environment. Upon the capability analysis, it was confirmed that the security items and specific indexes for the decision of security tendency could be extracted from structured and unstructured data. Key Words : Big data, Machine Learning, Sentiment Analysis, Data Mining, Machine Learning Algorithm

Mining User Queries with Markov Chains: Application to Online Image Retrieval

We propose a novel method for automatic annotation, indexing and annotation-based retrieval of images. The new method, that we call Markovian Semantic Indexing (MSI), is presented in the context of an online image retrieval system. Assuming such a system, the users' queries are used to construct an Aggregate Markov Chain (AMC) through which the relevance between the keywords seen by the system is defined. The users' queries are also used to automatically annotate the images. A stochastic distance between images, based on their annotation and the keyword relevance captured in the AMC, is then introduced. Geometric interpretations of the proposed distance are provided and its relation to a clustering in the keyword space is investigated. By means of a new measure of Markovian state similarity, the mean first cross passage time (CPT), optimality properties of the proposed distance are proved. Images are modeled as points in a vector space and their similarity is measured with MSI. The new method is shown to possess certain theoretical advantages and also to achieve better Precision versus Recall results when compared to Latent Semantic Indexing (LSI) and probabilistic Latent Semantic Indexing (pLSI) methods in Annotation-Based Image Retrieval (ABIR) tasks.

Competitive Poaching in Sponsored Search Advertising and Its Strategic Impact on Traditional Advertising

Traditional advertising, such as TV and print advertising, primarily builds awareness of a firm's product among consumers. On the other hand, sponsored search advertising can target consumers in a later stage of the purchase process because they self-identify themselves by searching for a relevant on a search engine. The ability to target these consumers who are closer to making a purchase induces competitors to poach these consumers by directly advertising on the firm's keywords. Using a game theory model, we study how competing firms should allocate their advertising budgets, and how a search engine should design the sponsored search position auction for deciding the order in which advertisers are displayed. We find that symmetric firms may follow asymmetric advertising strategies, with one firm focusing on traditional advertising and the other firm focusing on sponsored search with poaching. Interestingly, the search engine benefits from handicapping poaching, i.e., it benefits from discouraging competition in its own auctions. This explains why search engines such as Google, Yahoo! and Bing use keyword relevance scores to under-weight the bids of firms bidding on competitors' keywords. We also obtain various other interesting insights on the interplay between sponsored search advertising and traditional advertising.

Snippet Generation for Geographic Information Retrieval

Geographic information retrieval (GIR) aims at the retrieval of geographic-related documents based through the use of not only on keyword relevance but also on geographic relationships between the query and the geographic information in the texts. However, how to show search results in GIR has not been studied well, especially with regard to generating snippets that reflect the geographic part of the query. This paper proposes a novel snippet generation method. Our method first converts geographic phrases in the target text into geographic coordinates, then scores each of them according to their distance from the query using the coordinates. Next, it extracts fragments of the target text based on the distribution of the query keyword and geographic scores, and presents the combined fragments as a snippet. Evaluations are conducted with regard to two different aspects. Both attributes confirm the effectiveness of our method.

Geographic Information Retrieval for Just Your Surroundings

Geographic information retrieval (GIR) is a new research area that aims at the retrieval of geographic-related documents based not only on keyword relevance but also on geographic relationships between the query and the geographic information in texts. It is natural for people to want information related to just their surroundings. Conventional GIR systems, however, have relatively poor granularity, such as city or province, because they use geographic information in restricted ways -- mostly just for filtering. To address this problem, we propose a geographic scoring method that considers extent implied by each geographic names appeared in texts to emphasize geographic names that focus specific areas, rather than broad geographic names. Furthermore, to improve robustness against errors in pre-processing such as geo-parsing and geo-coding, we also propose a noise elimination method based on clustering. Evaluation is conducted using standard TREC-style evaluation metrics including MAP, R-precision, and so on. The results show that our method outperforms two baseline approaches: full-text search and using the nearest point in the text.