Detection and Evaluation of Cheating on College Exams using Supervised Classification

With the overwhelming increase in the amount of texts on the web, it is almost impossible for people to keep abreast of up-to-date information. Text mining is a process by which interesting information is derived from text through the discovery of patterns and trends. Text mining algorithms are used to guarantee the quality of extracted knowledge. However, the extracted patterns using text or data mining algorithms or methods leads to noisy patterns and inconsistency. Thus, different challenges arise, such as the question of how to understand these patterns, whether the model that has been used is suitable, and if all the patterns that have been extracted are relevant. Furthermore, the research raises the question of how to give a correct weight to the extracted knowledge. To address these issues, this paper presents a text post-processing method, which uses a pattern co-occurrence matrix to find the relation between extracted patterns in order to reduce noisy patterns. The main objective of this paper is not only reducing the number of closed sequential patterns, but also improving the performance of pattern mining as well. The experimental results on Reuters Corpus Volume 1 data collection and TREC filtering topics show that the proposed method is promising.

Background: While it is commonly recognized that almost every work and research discipline utilize their own taxonomy, the language used within a specific discipline may also vary depending on numerous factors, including the desired effect of the information being communicated and the intended audience. Different audiences are reached through publication of information, including research results, in different types of publication outlets such as newspapers, newsletters, magazines, websites, and journals. Prior research has shown that students, both undergraduate and graduate, as well as faculty may have a difficult time locating information in different publication outlet types (e.g., magazines, newspapers, journals). The type of publication may affect the ease of understanding and also the confidence placed in the acquired information. A text analytics tool for classifying the source of research as a newsletter (used as a substitute for newspaper articles), a magazine, or an academic journal article has been developed to assist students, faculty, and researchers in identifying the likely source type of information and classifying their own writings with respect to these possible publication outlet types. Literature Review: Literature on information literacy is discussed as this forms the motivation for the reported research. Additionally, prior research on using text mining and text analytics is examined to better understand the methodology employed, including a review of the original Scale of Theoretical and Applied Research system, adapted for the current research. Research Questions: The primary research question is: Can a text mining and text analytics approach accurately determine the most probable publication source type with respect to being from a newsletter, magazine, or journal? Methodology: A text mining and text analytics algorithm, STAR’ (System for Text Analytics-based Ranking), was developed from a previously researched text mining tool, STAR (Scale of Theoretical and Applied Research), that was used to classify the research type of articles between theoretical and applied research. The new text mining method, STAR’, analyzes the language used in manuscripts to determine the type of publication. This method first mines all words from corresponding publication source types to determine a keyword corpus. The corpus is then used in a text analytics process to classify full newsletters, magazine articles, and journal articles with respect to their publication source. All newsletters, magazine articles, and journal articles are from the library and information sciences (LIS) domain. Results: The STAR’ text analytics method was evaluated as a proof of concept on a specific LIS organizational newsletter, as well as articles from a single LIS magazine and a single LIS journal. STAR’ was able to classify the newsletters, magazine articles, and journal articles with 100% accuracy. Random samples from another similar LIS newsletter and a different LIS journal were also evaluated to examine the robustness of the STAR’ method in the initial proof of concept. Following the positive results of the proof of concept, additional journal, magazine, and newsletter articles were used to evaluate the generalizability of STAR’. The second-round results were very positive for differentiating journals and newsletters from other publication types, but revealed potential issues for distinguishing magazine articles from other types of publications. Discussion: STAR’ demonstrates that the language used for transferring information within a specific discipline does differ significantly depending on the intended recipients of the research knowledge. Further work is needed to examine language usage specific to magazine articles. Conclusions: The STAR’ method may be used by students and faculty to identify the likely source of research or discipline-specific information. This may improve trust in the reliability of information due to different levels of rigor applied to different types of publications. Additionally, the STAR’ classifications may be used by students, faculty, or researchers to determine the most appropriate type of outlet and correspondingly the most appropriate type of audience for the reported information in their own manuscripts, thereby improving the chance for successful sharing of information to appropriate audiences who will deem the information to be reliable, through publication in the most relevant outlet type.

Application of Text Mining to Extract Hotel Attributes and Construct Perceptual Map of Five Star Hotels from Online Review: Study of Jakarta and Singapore Five-Star Hotels

A mechanistic model was developed for brand talent demand analysis using text mining techniques in this study. Text mining is a powerful tool for extracting valuable insights from unstructured textual data, and its application in talent demand analysis offers significant potential for strategically informed decision-making in human resource management. The model integrated various technical theories related to text mining, including the Bag of Words model, Term Frequency-Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), and Gibbs sampling. These techniques enable the systematic analysis of textual data to identify patterns and trends in brand talent demand. The basic procedure involved in brand talent demand analysis was constructed using text-mining methodologies. The importance of data collection and preprocessing was emphasized based on web crawlers to gather relevant textual data from online sources. By leveraging web crawlers, the model acquired high-quality datasets essential for accurate analysis. Overall, the proposed mechanistic model provides a framework for brand talent demand analysis and contributes to the advancement of talent management strategies.

Text documents are the most common means for exchanging formal knowledge among people. Text is a rich medium that can contain a vast range of information, but text can be difficult to decipher automatically. Many organizations have vast repositories of textual data but with few means of automatically mining that text. Text mining methods seek to use an understanding of natural language text to extract information relevant to user needs. This article evaluates a new text mining methodology: prototypematching for text clustering, developed by the authors’ research group. The methodology was applied to four applications: clustering documents based on their abstracts, analyzing financial data, distinguishing authorship, and evaluating multiple translation similarity. The results are discussed in terms of common business applications and possible future research.

In this paper a system for voice of customer analysis is proposed, which will produce strong rules to help organization to take business decisions. It uses parallel association rule mining for rule generation and data usually tends to be very huge so partitioning is done on the basis of sentiment of customer. For this purpose text mining algorithm is used which extracts information from unstructured data. On these partitions of data association rule mining algorithm is applied which determines strong association rules and kept in a database. Domain experts can use these rules to take business decisions which can help an organization to have a better understanding of customer's all needs and wants.

News is new information or information about something that is currently happening, presented in the form of print media, broadcasts, social media or word of mouth to third parties or multiple people. The spread of this news is now very much happening both from social media or messages. However, the problem is that no one can guarantee the truth of the news received. Text mining is the application of data mining concepts and techniques to look for patterns in text, the process of analyzing text to find useful information for a particular purpose. Another definition related to text mining is that text mining is data mining in the form of text where the data source is usually obtained from documents and the goal is to find words that can represent the contents of the document so that an analysis of the connectivity between documents can be carried out. To make it easier to identify news hoax requires a text mining algorithm, using a text mining algorithm can be useful to get real news. Based on the calculations of the TF-IDF algorithm, it can be concluded that the similarity of the meaning of the news whose truth is not yet known is closest to the news ofmerahputih.com with a confidence level of 23.11%.

Both the environment and society are fundamentalaspects for businesses. Companies should consider these aspects while addressing challenges like transparency, accountability, sustainability and governance. This study aims at assessing the impact of information systems on Socioenvironmental Auditing Practices (SAP). For that purpose,it assumes that the impact of business activities on society and the environment can be audited, and the efficiency of such auditing practices depends on information systems. Datasets were mined from Scopus and Web of Science (WoS) using text mining methodology and were analyzed using the following tools: Correspondence Analysis (CA) to identify factors, challenges and technologies; Exploratory Factor Analysis (EFA) to identifyunderlying factor structures; Confirmatory Factor Analysis (CFA) to evaluate cause-and-effect relationships among factors, technologies and challenges. A few emerging technologies, viz., big data, blockchain, cloud computing solutions, machine learning, appear to have both a positive and a negative influence on SAP, but these are mostly insignificant. Challenges such as transparency, accountability, and sustainability were found to be associated with SAP. Blockchain technologiesinfluence social and environmental factors positively, whilemachine learning influencesEFAnegativelyyet its impact on CFA is positive and significant

Christian religious language in marketing communications is becoming more common, and many Christian-based service firms communicate religiosity via company websites. The impact of religious language in company websites has not been directly addressed, however. Consequently, using a novel text mining methodology, this research examines the presence and impact of religious language on organizations’ websites over a 10-year period, including the emotional sentiments of the language and their intensity. The most frequently used religious words are identified and discussed, as is how the religious language contributes to the emotional tone of the message.

Aims:This paper aims to conduct a Systematic Literature Review (SLR) of the relative applications of text mining in cybersecurity.Objectives:worldwide has been attributed to a change in the different activities associated with cyber security and demands a high automation level.Methods:In the cyber security domain, text mining is an alternative for improving the usefulness of various activities that entail unstructured data. This study searched databases of 516 papers from 2015 to 21. Out of which 75 papers are selected for analysis. A detailed evaluation of the selected studies employs source, techniques, and information extraction on cyber security applications.Results:This study extends gaps for future study such as text processing, availability of datasets, innovative methods, intelligent text mining.Conclusion:This study concludes with interesting findings of employing text mining in cybersecurity applications; the researchers need to exploit all related techniques and algorithms in text mining to detect and protect the organization from Cybersecurity applications.

Numerous e-news channels publish the daily happenings in the world from different sources. These huge amounts of news articles have lamentably conceived the information overload issue among the users. Hence text mining, which aims in extracting previously unknown information from unstructured text, has been widely used by several researchers to segregate full news articles however, the news headlines categorization is still specifically limited. Therefore, considering this limitation, the current research aims to propose a framework that will self-learn and automatically classify any given news headline into its corresponding news category using artificial intelligence methods i.e. text mining and machine learning algorithms. The proposed framework consists of three stages: Exploratory Data Analysis, Text Pre-processing, and Text Classification. For exploratory data analysis, the top 10 most frequent balanced news categories are chosen so that further processing of data can be done on a more balanced version of the dataset. After exploring the data, text pre-processing techniques are applied to make the data transformed, normalized, and structured. Finally, text classification is carried out with two approaches: unsupervised classification using Mean Shift and K-means algorithms and supervised classification using Logistic Regression with Bag of Words and TF-IDF algorithm. To depict the working of the proposed framework, a case study is presented on a news headlines dataset which accurately performed news headlines classification.

The current research is a systematic review of the literature on hotel marketing management that provides evidence that hotel marketing management contributes to organizational performance and satisfaction, and provides insights into how hotel operators can successfully implement it. This systematic review of the literature is based on the analysis of 417 papers via a text-mining methodology. Through cluster analysis, we divided the literature on hotel marketing management into four clusters, namely, literature regarding marketing reviews and frameworks, marketing strategies, services, and customers. These results pointed to some potential directions for future research in each cluster. This research can benefit researchers studying the current topics in the hotel marketing management field and help them recognize potential research areas. Additionally, it enables hoteliers to understand the benefits and processes of hotel marketing and defines the key elements of implementing a successful marketing campaign.

Inflammatory Bowel Diseases (IBDs), encompassing Ulcerative Colitis (UC) and Crohn's Disease (CD), are chronic, recurrent inflammatory conditions of the gastrointestinal tract. The microRNA (miRNA) -mRNA regulatory network is pivotal in the initiation and progression of IBDs. Although individual studies provide valuable insights into miRNA mechanisms in IBDs, they often have limited scope due to constraints in population diversity, sample size, sequencing platform variability, batch effects, and potential researcher bias. Our study aimed to construct comprehensive miRNA-mRNA regulatory networks and determine the cellular sources and functions of key miRNAs in IBD pathogenesis. To minimize potential bias from individual studies, we utilized a text mining-based approach on published scientific literature from PubMed and PMC databases to identify miRNAs and mRNAs associated with IBDs and their subtypes. We constructed miRNA-mRNA regulatory networks by integrating both predicted and experimentally validated results from DIANA, Targetscan, PicTar, Miranda, miRDB, and miRTarBase (all of which are databases for miRNA target annotation). The functions of miRNAs were determined through gene enrichment analysis of their target mRNAs. Additionally, we used two large-scale single-cell RNA sequencing datasets to identify the cellular sources of miRNAs and the association of their expression levels with clinical status, molecular and functional alternation in CD and UC. Our analysis systematically summarized IBD-related genes using text-mining methodologies. We constructed three comprehensive miRNA-mRNA regulatory networks specific to IBD, CD, and UC. Through cross-analysis with two large-scale scRNA-seq datasets, we determined the cellular sources of the identified miRNAs. Despite originating from different cell types, hsa-miR-142, hsa-miR-145, and hsa-miR-146a were common to both CD and UC. Notably, hsa-miR-145 was identified as myofibroblast-specific in both CD and UC. Furthermore, we found that higher tissue repair and enhanced glucose and lipid metabolism were associated with hsa-miR-145 in myofibroblasts in both CD and UC contexts. This comprehensive approach revealed common and distinct miRNA-mRNA regulatory networks in CD and UC, identified cell-specific miRNA expressions (notably hsa-miR-145 in myofibroblasts), and linked miRNA expression to functional alterations in IBD. These findings not only enhance our understanding of IBD pathogenesis but also offer promising diagnostic biomarkers and therapeutic targets for clinical practice in managing IBDs.

It is recognized that text information on Web is growing at an astounding pace. Research and application of text mining on Web is an important branch in the data mining. Now people mainly use information retrieval (IR) or the search engine to look up Web information. But IR focuses on searching for information that is explicitly present but not latent knowledge in some document, the search engine can hardly according to different need of different customers and provide individual service, and it is very difficult to mine data further. However, text mining on Web aims to resolve this problem. This paper discusses an Algorithm of how to follow the appointed website or Web page according to the user's request by using the text mining technique, how to extract and express text characteristic, how to classify the data information with feedback judgement combined with the Web page text contents for later use. We present experiments on different data set that demonstrate more effectiveness of our algorithm than traditional algorithm. The process of Web text mining, information extraction method, mining algorithm and realization technique are discussed in details.

The study presents information on why teacher educators in deaf education move from school classrooms to universities. These educators' priorities as university faculty are examined in regard to teaching, scholarship, and service; their scholarly productivity and perceptions of workplace conditions in school and university environments are studied. Findings show that these schoolteachers moved to higher education for various reasons, but primarily to pursue research and a scholarly life, and to have a greater influence on deaf and hard of hearing children and deaf education. As faculty, they are most interested in and committed to teaching; they publish at a modest rate. These educators perceive workplace conditions that support autonomy, flexible schedules, collegiality, and decision-making opportunities as more evident in university environments than in school environments. The researchers discuss the need for teacher educators in deaf education to pursue scholarly interests and to consider working with colleagues at the university and in schools to design collaborative research. Universities' need to support these efforts is also discussed.