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4.7 Text Categorization: Motivation

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Exploitation de text et analytique

Université de l'Illinois à Urbana-Champaign

4.4 (379 notes) | 42K étudiants inscrits

Cours 3 de 6 dans Exploration de données Spécialisation

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.

Visualiser le programme de cours

Compétences que vous apprendrez

Data Clustering Algorithms, Text Mining, Probabilistic Models, Sentiment Analysis

Avis

4.4 (379 notes)

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JS

Jun 07, 2017

The content was very useful, and the preparation of the course denoted much care and preparation by the teacher. I would love to see some modern topics like word embeddings covered in the course!

RC

Jun 25, 2017

very theoretical. very insightful too. finally got a glimpse how the features that I took for granted took shape ... thanks to the instructor, I love the textbook as well ~

À partir de la leçon

Week 4

During this module, you will learn text clustering, including the basic concepts, main clustering techniques, including probabilistic approaches and similarity-based approaches, and how to evaluate text clustering. You will also start learning text categorization, which is related to text clustering, but with pre-defined categories that can be viewed as pre-defining clusters.

4.1 Text Clustering: Motivation15:52

4.2 Text Clustering: Generative Probabilistic Models Part 116:18

4.3 Text Clustering: Generative Probabilistic Models Part 28:37

4.4 Text Clustering: Generative Probabilistic Models Part 314:55

4.5 Text Clustering: Similarity-based Approaches17:48

4.6 Text Clustering: Evaluation10:11

4.7 Text Categorization: Motivation14:37

4.8 Text Categorization: Methods11:50

4.9 Text Categorization: Generative Probabilistic Models31:18

Enseigné par

ChengXiang Zhai
Professor

Transcription

[SOUND]

This lecture is about text categorization.

In this lecture, we're going to talk about text categorization.

This is a very important technique for text data mining and analytics.

It is relevant to discovery of various different kinds of

knowledge as shown here.

First, it's related to topic mining and analysis.

And, that's because it has to do with

analyzing text to data based on some predefined topics.

Secondly, it's also related to opinion mining and sentiment analysis,

which has to do with discovery knowledge about the observer, the human sensor.

Because we can categorize the authors, for example,

based on the content of the articles that they have written, right?

We can, in general, categorize the observer

based on the content that they produce.

Finally, it's also related to text-based prediction.

Because, we can often use text categorization techniques to predict some

variables in the real world that are only remotely related to text data.

And so, this is a very important technique for text to data mining.

This is the overall plan for covering the topic.

First, we're going to talk about what is text categorization and

why we're interested in doing that in this lecture?

And now, we're going to talk about how to do text categorization for

how to evaluate the categorization results.

So, the problem of text categorization is defined as follows.

We're given a set of predefined categories possibly forming a hierarchy or so.

And often, also a set of training examples or

training set of labeled text objects which means the text

objects have already been enabled with known categories.

And then, the task is to classify any text object into one or

more of these predefined categories.

So, the picture on this slide shows what happens.

When we do text categorization,

we have a lot of text objects to be processed by a categorization system and

the system will, in general, assign categories through these documents.

As shown on the right and the categorization results,

and we often assume the availability of training examples and

these are the documents that are tag with known categories.

And these examples are very important for

helping the system to learn patterns in different categories.

And, this would further help the system then know how to recognize

the categories of new text objects that it has not seen.

So, here are some specific examples of text categorization.

And in fact, there are many examples, here are just a few.

So first, text objects can vary, so we can categorize a document,

or a passage, or a sentence, or collections of text.

As in the case of clustering, the units to be analyzed can vary a lot, so

this creates a lot of possibilities.

Secondly, categories can also vary.

Allocate in general, there's two major kinds of categories.

One is internal categories.

These are categories that categorize content of text object.

For example, topic categories or sentiment categories and

they generally have to do with the content of the text objects

throughout the categorization of the content.

The other kind is external categories that can characterize an entity

associated with the text object.

For example,

authors are entities associated with the content that they produce.

And so, we can use their content in determining which author has written,

which part, for example, and that's called author attribution.

Or, we can have any other mininal categories

associate with text data as long as there is minimal

connection between the entity and text data.

For example, we might collect a lot of reviews about a restaurant or

a lot of reviews about a product, and then,

this text data can help us infer properties of a product or a restaurant.

In that case, we can treat this as a categorization problem.

We can categorize restaurants or

categorize products based on their corresponding reviews.

So, this is an example for external category.

Here are some specific examples of the applications.

News categorization is very common as being started a lot.

News agencies would like to assign predefined

categories to categorize news generated everyday.

And, these virtual article categorizations are not important aspect.

For example, in the biomedical domain, there's MeSH annotations.

MeSH stands for Medical Subject Heading, and this is ontology of terms,

characterize content of literature articles in detail.

Another example of application is spam email detection or filtering, right?

So, we often have a spam filter

to help us distinguish spams from legitimate emails and

this is clearly a binary classification problem.

Sentiment categorization of product reviews or tweets is yet

another kind of applications where we can categorize, comparing to positive or

negative or positive and negative or neutral.

So, you can have send them to categories, assign the two text content.

Another application is automatic email routing or sorting, so,

you might want to automatically sort your emails into different folders and that's

one application of text categorization where each folder is a category.

The results are another important kind of applications of routing emails

to the right person to handle, so, in helpdesk,

email messaging is generally routed to a particular person to handle.

Different people tend to handle different kinds of requests.

And in many cases, a person would manually assign the messages to the right people.

But, if you can imagine, you can't be able to automatically

text categorization system to help routing request.

And, this is a class file, the incoming request in the one of the categories

where each category actually corresponds to a person to handle the request.

And finally, author attribution, as I just mentioned, is yet another application, and

it's another example of using text to actually infer properties of

some other entities.

And, there are also many variants of the problem formulation.

And so, first, we have the simplest case, which is a binary categorization,

where there are only two categories.

And, there are many examples like that, information retrieval or search engine.

Applications with one distinguishing relevant documents from non-relevant

documents for a particular query.

Spam filtering just distinguishing spams from non-spams, so, also two categories.

Sometimes, classifications of opinions can be in two categories,

positive and a negative.

A more general case would be K-category categorization and there are also

many applications like that, there could be more than two categories.

So, topic categorization is often such an example where you can have

multiple topics.

Email routing would be another example when you may have multiple folders or

if you route the email to the right person to handle it,

then there are multiple people to classify.

So, in all these cases, there are more than two kinds of categories.

Another variation is to have hierarchical categorization

where categories form a hierarchy.

Again, topical hierarchy is very common.

Yet another variation is joint categorization.

That's when you have multiple categorization tasks that are related and

then you hope to kind of join the categorization.

Further leverage the dependency of these tasks to improve accuracy for

each individual task.

Among all these binary categorizations is most fundamental and

part of it also is because it's simple and probably it's because

it can actually be used to perform all the other categorization tasks.

For example, a K-category categorization task can be actually

performed by using binary categorization.

Basically, we can look at each category separately and

then the binary categorization problem is whether object is in this category or

not, meaning in other categories.

And, the hierarchical categorization can also be done by progressively

doing flat categorization at each level.

So, we have, first, we categorize all the objects into, let's say,

a small number of high-level categories,

and inside each category, we have further categorized to sub-categories, etc.

So, why is text categorization important?

Well, I already showed that you, several applications but, in general,

there are several reasons.

One is text categorization helps enrich text representation and that's to achieve

more understanding of text data that's all it was useful for text analysis.

So, now with categorization text can be represented in multiple levels.

The keyword conditions that's often used for a lot text processing tasks.

But we can now also add categories and they provide two levels of transition.

Semantic categories assigned can also be directly or indirectly useful for

application.

So, for example, semantic categories could be already very useful or

other attribution might be directly useful.

Another example is when semantic categories can facilitate aggregation

of text content and this is another case of applications of text categorization.

For example, if we want to know the overall opinions about a product, we

could first categorize the opinions in each individual view

as positive or negative and then, that would allow us to easy to aggregate all

the sentiment, and it would tell us about

the 70% of the views are positive and 30% are negative, etc.

So, without doing categorization,

it will be much harder to aggregate such opinions to provide a concise

way of coding text in some sense based on all of the vocabulary.

And, sometimes you may see in some applications, text with categorizations

called a text coded, encoded with some control of vocabulary.

The second kind of reasons is to use text

categorization to infer properties of entities,

and text categories allows us to infer the properties

of such entities that are associate with text data.

So, this means we can use text categorization

to discover knowledge about the world.

In general, as long as we can associate the entity with text of data,

we can always the text of data to help categorize the corresponding entities.

So, it's used for

single information network that will connect the other entities with text data.

The obvious entities that can be directly connected are authors.

But, you can also imagine the author's affiliations or the author's age and

other things can be actually connected to text data indirectly.

Once we have made the connection, then we can make a prediction about those values.

So, this is a general way to allow us to use text mining through, so

the text categorization to discover knowledge about the world.

Very useful, especially in big text data analytics where we are often

just using text data as extra sets of data extracted from humans

to infer certain decision factors often together with non-textual data.

Specifically with text, for example,

we can also think of examples of inferring properties of entities.

For example, discovery of non-native speakers of a language.

And, this can be done by categorizing the content of speakers.

Another example is to predict the party affiliation of a politician based

on the political speech.

And, this is again an example of using text data to infer

some knowledge about the real world.

In nature, the problems are all the same, and

that's as we defined and it's a text categorization problem.

[MUSIC]

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