VISUALIZAÇÃO DE TEXTO MINERADO PARA EXTRAÇÃO DE REQUISITOS

This study aims to uncover recent trends and themes in text mining research, focusing on online news and annual report data sources. These sources are rich in content, real-world context, and domain-specific information, making them crucial for text mining. The primary research questions guiding this study are: 1) What are the emerging trends in text mining research applied to online news and annual reports? 2) What are the themes generated based on systematic literature review and text mining technique? To address this question, the study systematically reviews a large number of related studies using the PRISMA review protocol and text mining techniques from the SCOPUS and Web of Science databases. After thorough evaluation, 34 selected articles were analyzed. The PRISMA review protocol ensures transparency and completeness in reporting the review process through its standardized approach for systematic reviews. Additionally, this systematic review explores advancements in text mining techniques such as document clustering and topic modeling, which have facilitated the identification and extraction of relevant evidence from vast amounts of textual data. The study’s findings identified four primary themes (text mining/text analytics, machine learning, deep learning, ensemble methods) with 19 sub-themes related to each theme’s methodology when applying the PRISMA protocol. By utilizing a text mining techniques, five topics were uncovered based on article keywords (text mining, text analytics, machine learning, deep learning, and ensemble) and ten topics emerged based on article abstracts. Underlying both approaches is the consistent recognition of four main areas: text mining/text analytics, machine learning, deep learning, and ensemble methods. This systematic review offers a comprehensive overview of recent text mining research and the emerging trends in this field. It highlights the importance of systematic reviews in synthesizing existing literature and identifying areas for future research.

Nowadays, research in text mining has become one of the widespread fields in analyzing natural language documents. The present study demonstrates a comprehensive overview about text mining and its current research status. As indicated in the literature, there is a limitation in addressing Information Extraction from research articles using Data Mining techniques. The synergy between them helps to discover different interesting text patterns in the retrieved articles. In our study, we collected, and textually analyzed through various text mining techniques, three hundred refereed journal articles in the field of mobile learning from six scientific databases, namely: Springer, Wiley, Science Direct, SAGE, IEEE, and Cambridge. The selection of the collected articles was based on the criteria that all these articles should incorporate mobile learning as the main component in the higher educational context. Experimental results indicated that Springer database represents the main source for research articles in the field of mobile education for the medical domain. Moreover, results where the similarity among topics could not be detected were due to either their interrelations or ambiguity in their meaning. Furthermore, findings showed that there was a booming increase in the number of published articles during the years 2015 through 2016. In addition, other implications and future perspectives are presented in the study.

The ever-increasing scientific literature and the exponential growth of large-scale molecular data have prompted active research in biological text mining to facilitate literature-based curation of molecular databases. Meanwhile, systems biology and bio-ontologies are emerging as critical tools in biological research where complex data in disparate resources are generated, integrated and analyzed. Both rely on literature for data annotation and analysis. The challenges facing us are to develop broadly utilized text mining tools and systems, and to bring together developer and user communities for system development and evaluation. We describe a framework for linking text mining tools with ontology and systems biology, extending from a previously developed text mining resource, iProLINK. We focus on molecular and ontological resources, including genes/proteins, protein-protein interaction (PPI), and Protein Ontology. The framework consists of two major components: a user interface for text mining of PPI from an integrated tool server and software modules to allow text mining outputs to be created, ranked, and used by the community. Use cases are presented for assessing the gaps and making recommendations for future development.

Text mining is becoming vital as Web 2.0 offers collaborative content creation and sharing. Now Researchers have growing interest in text mining methods for discovering knowledge. Text mining researchers come from variety of areas like: Natural Language Processing, Computational Linguistic, Machine Learning, and Statistics. A typical text mining application involves preprocessing of text, stemming and lemmatization, tagging and annotation, deriving knowledge patterns, evaluating and interpreting the results. There are numerous approaches for performing text mining tasks, like: clustering, categorization, sentimental analysis, and summarization. There is a growing need to standardize the evaluation of these tasks. One major component of establishing standardization is to provide standard datasets for these tasks. Although there are various standard datasets available for traditional text mining tasks, but there are very few and expensive datasets for blog-mining task. Blogs, a new genre in web 2.0 is a digital diary of web user, which has chronological entries and contains a lot of useful knowledge, thus offers a lot of challenges and opportunities for text mining. In this paper, we report a new indigenous dataset for Pakistani Political Blogosphere. The paper describes the process of data collection, organization, and standardization. We have used this dataset for carrying out various text mining tasks for blogosphere, like: blog-search, political sentiments analysis and tracking, identification of influential blogger, and clustering of the blog-posts. We wish to offer this dataset free for others who aspire to pursue further in this domain.

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.

Polarity detection is an emerging area of research in text mining. Polarity detection is observing and identifying the sentiment inclination of text, whether it is positive or negative. In this paper, a fast mode of supervised learning for polarity detection on tweets is proposed, that is using datasets available in public. The feature selection strategy ensures reduced dimensionality. The low dimension data processing on Apache Spark supports scalability for large datasets. The experiment shows that the method is supporting high scalability and efficiency.KeywordsSentiment analysisPolarity detectionBig dataTwitter analysis

Submission to Canadian Government Consultation on a Modern Copyright Framework for AI and the Internet of Things

everyday life communication interaction among people leading to mutual learning and sharing of valuable knowledge, such as chat, messaging, comments, and posts on board etc. Also, social networking websites, search engines sharing huge data texts in websites. The text is nothing but the combination of characters. Therefore, analyzing and extracting information patterns from such data sets are more complex. Several methods have been proposed for analyzing such texts and extracting information. In this paper, we present different text mining techniques to discover various textual patterns from the different sources. This topic is also deals with the areas such as information retrieval, machine learning, statistics, computational data sciences and advanced data mining. We also discuss future challenges of this area using different techniques, particularly rough set based text mining techniques, improvements and research directions in this paper. Keywordsmining, Text mining, Rough sets,Classification, Summarization, and Text categorization.

Glaucoma is a silent eye disease that steals sight without warning and it is the second leading cause of blindness worldwide. It cannot be cured but it can be controlled. A gene network analysis to understand the cross talk between glaucoma networks of genes could contribute to better understanding and treatment of the disease. In this paper, we extend our previous text mining and network analysis research for extraction of a more comprehensive glaucoma interaction network from PubMed Central articles. After text mining of the PubMed Central literature to extract potential sentences bearing associations between glaucoma genes in the literature, the mined associations are used to construct a glaucoma gene interaction network and a network analysis is applied to understand the building structure of the network. The network analysis reveals a succinct network composed of five distinct clusters associated with seven benchmark glaucoma biomarkers. The knowledge that can be extracted from such a network could provide a useful summative knowledge base to complement other forms of clinical information related to this disease that affects many diagnosed individuals worldwide.

Generalized plaid models

This paper presents our research in text mining for discovering important engineering fault diagnostic knowledge from unstructured and verbatim text descriptions. In particular we focus on developing machine learning algorithms for detecting documents that contain descriptions of systematic failures and root causes to the faults. We developed a machine algorithm based on entropy analysis to extract an A-word list, a list of words that are important to characterize the documents of interests, a vector space model to represent features of important documents, and a constraint based k-means clustering algorithm to generate high purity clusters for use in detecting important documents. We applied the algorithms to automotive diagnostic text data, which are unstructured and verbatim descriptions by customers and technicians that contain many typos and self-invented terms. We were able to reduce a list of 2183 words to a list of 137 important words. The classification system generated by these machine learning algorithms showed high recall and accuracy in detecting important diagnostic descriptions.

The discipline of text and data mining (TDM) is growing in visibility in the scholarly research community. In May 2014, CrossRef launched CrossRef Text and Data Mining Services, which aims to alleviate the issues that text mining researchers encounter when trying to collect the corpus of content that they want to mine from the different publishers who have produced it. This article will discuss what the CrossRef service does, the problem that it is trying to solve and the current status of the rollout of the service across CrossRef member publishers.

Text mining, a subfield of natural language processing (NLP), has received considerable attention in recent yearsdue to its ability to extract valuable insights from large volumes of unstructured textual data. This review aims toprovide a comprehensive evaluation of the applicability of text mining techniques across various domains andindustries.The review starts off with a dialogue of the basic ideas and methodologies that are concerned with textual contentmining together with preprocessing, feature extraction, and machine learning algorithms.Furthermore, this survey highlights the challenges faced at some stage in implementing textual content miningstrategies. Additionally, the review explores emerging tendencies and possibilities in text-mining research. Itdiscusses advancements in deep learning models for text evaluation, integration with different AI technologies likeimage or speech recognition for multimodal analysis, utilization of domain-unique ontologies or information graphsfor more desirable information of textual facts, and incorporation of explainable AI strategies to improveinterpretability. The findings from this overview are analyzed to identify common developments and patterns in textmining packages across extraordinary domain names.The consequences of this paper will advantage researchers by means of imparting updated expertise of modernpractices in textual content mining. Additionally, it will manual practitioners in selecting suitable strategies for theirunique application domain names while addressing capacity-demanding situations.

The Research of Text Mining Based on Self-Organizing Maps

Recent advances in text mining have provided new methods for capitalizing on the voluminous natural language text data created by organizations, their employees, and their customers. Although often overlooked, decisions made during text preprocessing affect whether the content and/or style of language are captured, the statistical power of subsequent analyses, and the validity of insights derived from text mining. Past methodological articles have described the general process of obtaining and analyzing text data, but recommendations for preprocessing text data were inconsistent. Furthermore, primary studies use and report different preprocessing techniques. To address this, we conduct two complementary reviews of computational linguistics and organizational text mining research to provide empirically grounded text preprocessing decision-making recommendations that account for the type of text mining conducted (i.e., open or closed vocabulary), the research question under investigation, and the data set’s characteristics (i.e., corpus size and average document length). Notably, deviations from these recommendations will be appropriate and, at times, necessary due to the unique characteristics of one’s text data. We also provide recommendations for reporting text mining to promote transparency and reproducibility.