Text mining in the identification of duties and responsibilities of the project manager (original) (raw)

Insights from It Jobs Market with Text Mining Approach

SEA: Practical Application of Science, 2020

On the labor market, IT Jobs represent one of the most important domains. This paper analyzed the IT Jobs from a large collection of job listings from a Romanian website. The Web Crawling techniques were used to extract the data from the website, the Text Mining, World Cloud and statistic techniques to analyze and present the results. Insights that are required for an IT Job were extracted. The results highlight the following: the most required IT Job Type was the Full Time one, the Career Level was the Mid Level and the Study Level was the Graduated one. The Text Mining Approach revealed that the most frequent words for the IT Jobs offers were: team, work, development, project, experience, environment, service, skill, customer, knowledge and software. A comparison between terms in IT Jobs in English language versus IT Jobs in Romanian language was also performed. As conclusion, in this paper, by combining different techniques to extract and analyze the textual data set of vacancy o...

The Ideal Candidate. Analysis of Professional Competences through Text Mining of Job Offers

2006

Summary. The aim of this paper is to propose analytical tools for identifying peculiar aspects of the job market for graduates. The main objective is to reduce the complexity of the phenomenon, both on the variable side, by transforming the collected information into latent factors, and on the unit side, by classifying observations. We propose a strategy for dealing with data that have different source and nature. The dependence structure is investigated to identify potential evolutionary paths.

Automatic identification of competences expected by employers with the use of exploratory text analysis

2018

Exploratory text analysis allows to identify semantic components present in processing documents. For every component it is possible to describe its character and to evaluate its importance. Using the approach presented above for automatic analysis of job offers it is possible do discover components which are common for all texts and to estimate their importance in every offer. Unfortunately, semantic components obtained with the help of text mining algorithms, usually do not reflect competences within the meaning of specialists from the HR area. In the paper authors are going to present a method which will be able to identify in a set of job offers semantic components corresponding to professional, social, personal or managerial competences. Also the method of competence description and evaluation will be proposed. The computational model used for the analysis is composed of the two parts. The first is formed by the Latent Dirichlet Allocation Model and identifies latent semantic c...

Project Management using Data Mining 1

2015

It could be a huge challenge to ensure the standard of discovered connection options in text documents for describing user preferences due to giant scale terms and information patterns. Most existing in style text mining and classification strategies has adopted term-based approaches. However, they need all suffered from the issues of ambiguity and synonymousness. Over the years, there has been usually command the hypothesis that pattern-based strategies ought to perform higher than term-based ones in describing user preferences; nonetheless, a way to effectively use giant scale patterns remains a tough downside in text mining. to create a breakthrough during this difficult issue, this paper presents associate innovative model for connection feature discovery. It discovers each positive and negative patterns in text documents as higher level options and deploys them over low-level options (terms). It conjointly classifies terms into classes and updates term weights supported their s...

A Comprehensive Study of Text Mining Approach.pdf

International Journal of Computer Science and Network Security, 2016

Text mining or knowledge discovery is that sub process of data mining, which is widely being used to discover hidden patternsand significant information from the huge amount of unstructured written material. The proliferation of clouds, research and technologies are responsible for the creation of vast volumes of data. This kind of data cannot be used until or unless specific information or pattern is discovered. For this text mining uses techniques of different fields like machine learning, visualization, case-based reasoning, text analysis, database technology statistics, knowledge management, natural language processing and information retrieval. Text mining is largely growing field of computer science simultaneously to big data and artificial intelligence. This paper contains the review of text mining techniques, tools and various applications.

Textual Data Mining For Knowledge Discovery and Data Classification: A Comparative Study

Business Intelligence solutions are key to enable industrial organisations (either manufacturing or construction) to remain competitive in the market. These solutions are achieved through analysis of data which is collected, retrieved and re-used for prediction and classification purposes. However many sources of industrial data are not being fully utilised to improve the business processes of the associated industry. It is generally left to the decision makers or managers within a company to take effective decisions based on the information available throughout product design and manufacture or from the operation of business or production processes. Substantial efforts and energy are required in terms of time and money to identify and exploit the appropriate information that is available from the data. Data Mining techniques have long been applied mainly to numerical forms of data available from various data sources but their applications to analyse semi-structured or unstructured databases are still limited to a few specific domains. The applications of these techniques in combination with Text Mining methods based on statistical, natural language processing and visualisation techniques could give beneficial results. Text Mining methods mainly deal with document clustering, text summarisation and classification and mainly rely on methods and techniques available in the area of Information Retrieval (IR). These help to uncover the hidden information in text documents at an initial level. This paper investigates applications of Text Mining in terms of Textual Data Mining (TDM) methods which share techniques from IR and data mining. These techniques may be implemented to analyse textual databases in general but they are demonstrated here using examples of Post Project Reviews (PPR) from the construction industry as a case study. The research is focused on finding key single or multiple term phrases for classifying the documents into two classes i.e. good information and bad information documents to help decision makers or project managers

Text mining of Post Project Reviews

2008

Post Project Reviews (PPR) are a rich source of knowledge and information for organisations-if they have the time and resources to analyse them. Too often such reports are stored, unread by many who can benefit from them. PPRs attempt to document the project experience-both good and bad. If these reports were analysed collectively, they may expose important detail, perhaps repeated between projects. However, because most companies do not have the resources to examine these PPR, either individually or collectively, important insights are missed thereby leading to a missed opportunity to learn from previous projects. Hidden knowledge and experiences can be captured by using knowledge discovery and text mining to uncover patterns, associations, and trends in data. The results might then be used to enhance processes, improve customer relationships, and identify specific problem areas to address. This paper outlines an ongoing research project that investigates the use of knowledge discovery and text mining on Post Project Reviews. An illustrative example will be presented using case studies from the construction sector. The PPR processes of two construction companies were mapped with the aim of understanding the context, format, terminologies used and key knowledge areas suitable for text mining. The textual examination of the PPR reports was complemented by semi-structured interviews and workshops to understand the production and content of the reports. Preliminary results highlight that although organisations have publicised, standard processes for PPR, there is a variance in how these are conducted and produced on a regional basis. These variances provide a number of challenges for organisations from a corporate perspective. Also, there is an over-reliance on key individuals with little attempt to make some of their knowledge more explicit and therefore easier to disseminate between project team members. This paper summarises the challenges in identifying the type of knowledge to be text mined, the format of PPR reports and the process of conducting PPR. It will also highlights the development of suitable 2 ontologies for text mining PPR reports and provides recommendations on how to improve the PPR process of companies.

A Survey Paper on Text Mining-Techniques , Applications And Issues *

Rapid progress in digital data acquisition techniques have led to huge volume of data. More than 80 percent of today's data is composed of unstructured or semi-structured data. The recovery of similar patterns and trends to see the text data from huge volume of data is a big issue. Text mining is a process of extracting interesting and nontrivial patterns from huge amount of text documents. There lies many techniques and tools to mine the text documents and discover the information for future and process in decision making. The choice of selecting the right and appropriate text mining technique helps to recover the speed and slows the time and effort required to get valuable information.This paper briefly discusses and analyze the text mining techniques and their applications. With the advancement of technology, more and more data is available in digital form. Among which, most of the data (approx. 85%) is in unstructured textual form. Thus, it has become essential to build better techniques and algorithms to get useful and interesting data from the large amount of textual data.Hence, the field of information extraction and text mining became popular areas of research, to get interesting and needful information.

A Survey on Text Mining - Techniques, Application

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023

Text mining, also known as text data mining or text analytics, is a field of study that focuses on extracting meaningful information and knowledge from textual data. The rapid advancement of digital data acquisition techniques has resulted in an unprecedented volume of data. In fact, over 80 percent of the data generated today comprises unstructured or semi-structured formats. Extracting meaningful patterns and trends from such massive amounts of text data poses a significant challenge. Text mining addresses this challenge by extracting valuable and nontrivial patterns from vast collections of text documents. Various techniques and tools are available for mining text documents and uncovering valuable information to inform decision-making and future processing. Selecting the appropriate text mining technique is crucial as it can significantly enhance the speed and efficiency of retrieving valuable information, reducing the time and effort required. This paper provides a concise analysis and discussion of text mining techniques and their applications. As technology continues to advance, the availability of digital data continues to increase. A substantial portion, approximately 85 percent, of this data exists in unstructured textual form. Consequently, it has become imperative to develop improved techniques and algorithms to effectively extract useful and interesting information from these vast amounts of textual data. This has resulted in the emergence of information extraction and text mining as popular research areas dedicated to uncovering valuable and necessary information from textual data.

A STUDY OF TEXT MINING METHODS, APPLICATIONS,AND TECHNIQUES

Data mining is used to extract useful information from the large amount of data. It is used to implement and solve different types of research problems. The research related areas in data mining are text mining, web mining, image mining, sequential pattern mining, spatial mining, medical mining, multimedia mining, structure mining and graph mining. Text mining also referred to text of data mining, it is also called knowledge discovery in text (KDT) or knowledge of intelligent text analysis. The process is driving high-quality information from not-structured to semi-structured data. Text mining is the discovery by automatically extracting information from different written resources and also by computer for extracting new, previously unknown information. This paper discusses about the process of text mining, methods, tools, applications and techniques.