Introduction to Text Mining and Analytics

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Del curso dictado por University of Illinois at Urbana-Champaign

Text Mining and Analytics

340 calificaciones

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University of Illinois at Urbana-Champaign

Text Mining and Analytics

340 calificaciones

Curso 3 de 6 en SpecializationData Mining

This course will cover the major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort. Detailed analysis of text data requires understanding of natural language text, which is known to be a difficult task for computers. However, a number of statistical approaches have been shown to work well for the "shallow" but robust analysis of text data for pattern finding and knowledge discovery. You will learn the basic concepts, principles, and major algorithms in text mining and their potential applications.

De la lección

Orientation

You will become familiar with the course, your classmates, and our learning environment. The orientation will also help you obtain the technical skills required for the course.

Introduction to Text Mining and Analytics8:15

Course Prerequisites & Completion6:34

Conoce a los instructores

ChengXiang Zhai
Professor
Department of Computer Science

[SOUND]

Hello.

Welcome to the course Text Mining and Analytics.

My name is ChengXiang Zhai.

I have a nickname, Cheng.

I am a professor of the Department of Computer Science at the University of

Illinois at Urbana-Champaign.

This course is a part of a data mining specialization

offered by the University of Illinois at Urbana-Champaign.

In addition to this course, there are four other courses offered by

Professor Jiawei Han, Professor John Hart and me, followed by

a capstone project course that all of us will teach together.

This course is particularly related to another course in the specialization,

mainly text retrieval and search engines in that both courses are about text data.

In contrast, pattern discovery and cluster analysis are about

algorithms more applicable to all kinds of data in general.

The visualization course is also relatively general in that the techniques

can be applied to all kinds of data.

This course addresses a pressing need for harnessing big text data.

Text data has been growing dramatically recently,

mostly because of the advance of technologies deployed on the web

that would enable people to quickly generate text data.

So, I listed some of the examples on this slide

that can show a variety of text data that are available today.

For example, if you think about the data on the internet, on the web,

everyday we are seeing many web pages being created.

Blogs are another kind of new text data that

are being generated quickly by people.

Anyone can write a blog article on the web.

New articles of course have always been

a main kind of text data that being generated everyday.

Emails are yet another kind of text data.

And literature is also representing a large portion of text data.

It's also especially very important because of the high quality

in the data.

That is, we encode our knowledge about the word

using text data represented by all the literature articles.

It's a vast amount of knowledge of

all the text and data in these literature articles.

Twitter is another representative text data representing social media.

Of course there are forums as well.

People are generating tweets very quickly

indeed as we are speaking perhaps many people have already written many tweets.

So, as you can see there are all kinds of text data

that are being generated very quickly.

Now these text data present some challenges for people.

It's very hard for anyone to digest all the text data quickly.

In particular, it's impossible for scientists to read all of the for

example or for anyone to read all the tweets.

So there's a need for tools to help people digest text data more efficiently.

There is also another interesting opportunity

provided by such big text data, and that is it's possible to leverage

the amount of text data to discover interesting patterns to

turn text data into actionable knowledge that can be useful for decision making.

So for example, product managers may be interested

in knowing the feedback of customers about their products,

knowing how well their products are being received as

compared with the products of competitors.

This can be a good opportunity for

leveraging text data as we have seen a lot of reviews of product on the web.

So if we can develop a master text mining techniques to tap into such

a [INAUDIBLE] to extract the knowledge and opinions of people about these products,

then we can help these product managers to gain business intelligence or

to essentially feedback from their customers.

In scientific research, for example,

scientists are interested in knowing the trends of research topics, knowing

about what related fields have discovered.

This problem is especially important in biology research as well.

Different communities tend to use different terminologies, yet

they're starting very similar problems.

So how can we integrate the knowledge that is covered in different communities

to help study a particular problem?

It's very important, and it can speed up scientific discovery.

So there are many such examples where we can leverage the text data

to discover useable knowledge to optimize our decision.

The main techniques for

harnessing big text data are text retrieval and text mining.

So these are two very much related technologies.Yet,

they have somewhat different purposes.

These two kinds of techniques are covered in the tool in this specialization.

So, text retrieval on search engines covers text retrieval,

and this is necessary to turn big text data into

a much smaller but more relevant text data, which are often the data that

we need to handle a particular problem or to optimize a particular decision.

This course covers text mining which is a second step in this pipeline

that can be used to further process the small amount of relevant data

to extract the knowledge or to help people digest the text data easily.

So the two courses are clearly related, in fact,

some of the techniques are shared by both text retrieval and text mining.

If you have already taken the text retrieval course, then you might see

some of the content being repeated in this text mining course, although

we'll be talking about the techniques from a very different perspective.

If you have not taken the text retrieval course,

it's also fine because this course is self-contained and

you can certainly understand all of the materials without a problem.

Of course, you might find it beneficial to take both courses and

that will give you a very complete set of skills to handle big text data.

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