how can text mining be used in security and counterterrorism? course hero

What is the purpose of text mining?

This text mining tool is the proceedings and to show how they change over time. It is also used as topic tracking. Therefore, initiatives have without removing publisher barriers to public access. research application area for text mining. In the year 2005, of drug targets are der ived from the literature.

Should the government use data mining and data analysis to fight terrorism?

Data-mining and automated data-analysis techniques are powerful tools for intelligence and law enforcement officials fighting terrorism. But these tools also generate controversy and concern. They make analysis of data--including private data--easier and more powerful, and this can make private data more useful and attractive to the government.

What are data mining algorithms and how do they work?

Data mining algorithms are used to manage organized data sets and web mining algorithms can be helpful in mining and extracting from unstructured web pages and text data that is available across the web. Websites built in different platforms have varying data structures and that makes it quite difficult to read for a single algorithm.

What is the difference between bio-entity recognition and text mining?

The goal of text mining in this area is to more efficient manner. One o nline text mining application service. Bio-entity recognition aims to identify and classify biologists. Entity rec ognition is becoming increasingly to high throughput experimental methods. It can be used in

What is text mining?

Text mining is an analytical field which derives high quality information from text. Text mining is widely used in the industry when data is unstructured. Derived information can be provided in the form of numbers (indices), categories or clusters, summary of text. In this blog, we will focus on applications of text mining, workflow and example.

What is text mining in Outlook?

In outlook, a user categorizes the emails into various folders/spam. Similarly, on a larger scale using text mining algorithms key topics can be identified and the emails can be automatically forwarded to desired department

What is text cleanup?

1. Text cleanup- Removes hyperlinks, special characters, ads from web pages, remove figures and formulas from web pages and documents. 2. Tokenization- Tokenization is the process to divide unstructured data into tokens such as words, phrase, keywords, and other elements. 3.

How does text mining work?

Text mining works by transposing words and phrases in unstructured data into numerical values which can then be linked with structured data in a database and analyzed with traditional data mining techniques. Text information retrieval and data mining has thus become increasingly important.

Why is text mining important?

Text mining has become an exciting research field as it tries to discover valuable information from unstructured texts. The unstructured texts which contain vast amount of information cannot simply be used for further processing by computers. Therefore, exact processing methods, algorithms and techniques are vital in order to extract this valuable ...

What is the use of ICT in business intelligence?

Summary: The use of modern ICT solutions of business intelligence is becoming more and more common, and are also increasingly used in the field of legal regulations, and above all in legal information management , advanced ICT solutions. The purpose of this article is to indicate the possibility of implementing modern ICT tools in order to obtain the knowledge contained in the judicial decisions. Court decisions, as one of the areas of creating and applying law, are a source of information resources for numerous groups of stakeholders. Therefore, the creation and use of IT tools to support the processes of gaining knowledge about law from court decisions is a legitimate direction in the development of legal informatics. The article is the result of literature research dedicated to the applications of ICT solutions in the area of creating and distributing legal information. Keywords: judicial decisions, data mining, ICT, knowledge, gaining knowledge, legal informatics. Streszczenie: Wykorzystanie nowoczesnych rozwiązań ICT klasy business intelligence staje się coraz bardziej powszechne. Również w obszarze regulacji prawnych, a przede wszystkim w zarządzaniu informacją prawną zaawansowane rozwiązania ICT mają coraz szersze zastosowanie. Celem niniejszego artykułu jest wskazanie możliwości implementowania nowoczesnych narzędzi ICT w celu pozyskania wiedzy zawartej w orzecznictwie sądowym. Orzecznictwo sądowe jako jeden z obszarów tworzenia oraz stosowania prawa stanowi źródło zasobów informacyjnych, które są wykorzystywane przez liczne grupy interesariuszy. Dlatego też tworzenie oraz wykorzystanie informatycznych narzędzi w celu wspomagania procesów odkrywania wiedzy o prawie z orzeczeń sądowych stanowi zasadny kierunek rozwoju informatyki prawniczej. Artykuł powstał w wyniku badań literaturowych dotyczących zastosowania rozwiązań ICT w obszarze tworzenia oraz dystrybucji informacji prawnej. Słowa kluczowe: orzeczenia sądowe, data mining, ICT, wiedza, odkrywanie wiedzy, informatyka prawnicza.

How does crowdsourcing help in the industrial revolution?

Due to new competence requirements arising from technical and process innovations in the industrial setting crowdsourcing might provide the means to acquire and integrate these competences in a flexible, temporary way and open up new, decentralized innovation sources. Furthermore, not all required competences can be hold inside due to efficiency reasons. Thus, future-oriented companies must decide whether to systematically develop the competences of their employees by themselves or to outsource-crowdsource-value-adding processes and tasks. However, it is not clear how and to which extend crowdsourcing-based solutions can be systematically integrated into innovation and production processes. Furthermore, the diverse relationships between crowdsourcing and Industry 4.0 are not sufficiently understood for realizing entrepreneurial and societal benefits. Thus, the underlying research question is: What is the relationship between crowdsourcing and Industry 4.0 and how can synergies be levered? To Answer this question, the applied methodology combines a systematical literature review with a text mining approach for the analysis of large text bodies. The results of the study will provide a first benchmark on the frequency and type of use of crowdsourcing technologies. Moreover, this review provides a first guideline how and where to use crowdsourcing technologies in companies that are operating in the industry 4.0 sector.

What is stemming in text analytics?

Stemming is the process to convert words into their root words by the stemming algorithm. It is one of the main processes in text analytics where the text data needs to go through stemming process before proceeding to further analysis. Text analytics is a very common practice nowadays that is practiced toanalyze contents of text data from various sources such as the mass media and media social. In this study, two different stemming techniques; Porter and Lancaster are evaluated. The differences in the outputs that are resulted from the different stemming techniques are discussed based on the stemming error and the resulted visualization. The finding from this study shows that Porter stemming performs better than Lancaster stemming, by 43%, based on the stemming error produced. Visualization can still be accommodated by the stemmed text data but some understanding of the background on the text data is needed by the tool users to ensure that correct interpretation can be made on the visualization outputs.

What is content in text?

content. It is a collectio n of text documents, the process of

When did text categorizatio n start?

text categorizatio n starts early 1960s. It is a hot topic in