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Text Mining. What is Text Mining?. There are many examples of text-based documents (all in ‘electronic’ format…) e-mails, corporate Web pages, customer surveys, résumés, medical records, DNA sequences, technical papers, incident reports, news stories and more…

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text mining

Text Mining

Data Mining -Volinsky - 2011 - Columbia University

what is text mining
What is Text Mining?
  • There are many examples of text-based documents (all in ‘electronic’ format…)
    • e-mails, corporate Web pages, customer surveys, résumés, medical records, DNA sequences, technical papers, incident reports, news stories and more…
  • Not enough time or patience to read
    • Can we extract the most vital kernels of information…
  • So, we wish to find a way to gain knowledge (in summarised form) from all that text, without reading or examining them fully first…!
    • Some others (e.g. DNA seq.) are hard to comprehend!

Data Mining -Volinsky - 2011 - Columbia University

what is text mining3
What is Text Mining?
  • Traditional data mining uses ‘structured data’ (n x p matrix)
  • The analysis of ‘free-form text’ is also referred to as ‘unstructured data’,
    • successful categorisation of such data can be a difficult and time-consuming task…
  • Often, can combine free-form text and structured data to derive valuable, actionable information… (e.g. as in typical surveys) – semi-structured

Data Mining -Volinsky - 2011 - Columbia University

text mining examples
Text Mining: Examples
  • Text mining is an exercise to gain knowledge from stores of language text.
  • Text:
    • Web pages
    • Medical records
    • Customer surveys
    • Email filtering (spam)
    • DNA sequences
    • Incident reports
    • Drug interaction reports
    • News stories (e.g. predict stock movement)

Data Mining -Volinsky - 2011 - Columbia University

what is text mining5
What is Text Mining
  • Data examples
    • Web pages
    • Customer surveys

Data Mining -Volinsky - 2011 - Columbia University

amazon com
Amazon.com

Data Mining -Volinsky - 2011 - Columbia University

of mice and men concordance
Of Mice and Men: Concordance

Concordance is an alphabetized list of the most frequently occurring words in a book, excluding common words such as "of" and "it." The font size of a word is proportional to the number of times it occurs in the book.

Data Mining -Volinsky - 2011 - Columbia University

of mice and men text stats
Of Mice and Men: Text Stats

Data Mining -Volinsky - 2011 - Columbia University

slide9

Text Mining: Yahoo Buzz

Data Mining -Volinsky - 2011 - Columbia University

text mining google news
Text Mining: Google News

Data Mining -Volinsky - 2011 - Columbia University

text mining11
Text Mining
  • Typically falls into one of two categories
    • Analysis of text: I have a bunch of text I am interested in, tell me something about it
      • E.g. sentiment analysis, “buzz” searches
    • Retrieval: There is a large corpus of text documents, and I want the one closest to a specified query
      • E.g. web search, library catalogs, legal and medical precedent studies

Data Mining -Volinsky - 2011 - Columbia University

text mining analysis
Text Mining: Analysis
  • Which words are most present
  • Which words are most surprising
  • Which words help define the document
  • What are the interesting text phrases?

Data Mining -Volinsky - 2011 - Columbia University

text mining retrieval
Text Mining: Retrieval
  • Find k objects in the corpus of documents which are most similar to my query.
  • Can be viewed as “interactive” data mining - query not specified a priori.
  • Main problems of text retrieval:
    • What does “similar” mean?
    • How do I know if I have the right documents?
    • How can I incorporate user feedback?

Data Mining -Volinsky - 2011 - Columbia University

text retrieval challenges
Text Retrieval: Challenges
  • Calculating similarity is not obvious - what is the distance between two sentences or queries?
  • Evaluating retrieval is hard: what is the “right” answer ? (no ground truth)
  • User can query things you have not seen before e.g. misspelled, foreign, new terms.
  • Goal (score function) is different than in classification/regression: not looking to model all of the data, just get best results for a given user.
  • Words can hide semantic content
    • Synonymy: A keyword T does not appear anywhere in the document, even though the document is closely related to T, e.g., data mining
    • Polysemy: The same keyword may mean different things in different contexts, e.g., mining

Data Mining -Volinsky - 2011 - Columbia University

basic measures for text retrieval
Basic Measures for Text Retrieval
  • Precision: the percentage of retrieved documents that are in fact relevant to the query (i.e., “correct” responses)
  • Recall: the percentage of documents that are relevant to the query and were, in fact, retrieved

Data Mining -Volinsky - 2011 - Columbia University

precision vs recall
Precision vs. Recall
  • We’ve been here before!
    • Precision = TP/(TP+FP)
    • Recall = TP/(TP+FN)
    • Trade off:
      • If algorithm is ‘picky’: precision high, recall low
      • If algorithm is ‘relaxed’: precision low, recall high
    • BUT: recall often hard if not impossible to calculate

actual

outcome

predicted

outcome

16

Data Mining -Volinsky - 2011 - Columbia University

precision recall curves
Precision Recall Curves
  • If we have a labelled training set, we can calculate recall.
  • For any given number of returned documents, we can plot a point for precision vs. recall. (similar to thresholds in ROC curves)
  • Different retrieval algorithms might have very different curves - hard to tell which is “best”

Data Mining -Volinsky - 2011 - Columbia University

term document matrix
Term / document matrix
  • Most common form of representation in text mining is the term - document matrix
    • Term: typically a single word, but could be a word phrase like “data mining”
    • Document: a generic term meaning a collection of text to be retrieved
    • Can be large - terms are often 50k or larger, documents can be in the billions (www).
    • Can be binary, or use counts

Data Mining -Volinsky - 2011 - Columbia University

term document matrix19
Term document matrix

Example: 10 documents: 6 terms

  • Each document now is just a vector of terms, sometimes boolean

Data Mining -Volinsky - 2011 - Columbia University

term document matrix20
Term document matrix
  • We have lost all semantic content
  • Be careful constructing your term list!
    • Not all words are created equal!
    • Words that are the same should be treated the same!
  • Stop Words
  • Stemming

Data Mining -Volinsky - 2011 - Columbia University

stop words
Stop words
  • Many of the most frequently used words in English are worthless in retrieval and text mining – these words are called stop words.
    • the, of, and, to, ….
    • Typically about 400 to 500 such words
    • For an application, an additional domain specific stop words list may be constructed
  • Why do we need to remove stop words?
    • Reduce indexing (or data) file size
      • stopwords accounts 20-30% of total word counts.
    • Improve efficiency
      • stop words are not useful for searching or text mining
      • stop words always have a large number of hits

Data Mining -Volinsky - 2011 - Columbia University

stemming
Stemming
  • Techniques used to find out the root/stem of a word:
    • E.g.,
        • user engineering
        • users engineered
        • used engineer
        • using
  • stem: use engineer

Usefulness

  • improving effectiveness of retrieval and text mining
    • matching similar words
  • reducing indexing size
    • combing words with same roots may reduce indexing size as much as 40-50%.

Data Mining -Volinsky - 2011 - Columbia University

basic stemming methods
Basic stemming methods
  • remove ending
    • if a word ends with a consonant other than s,

followed by an s, then delete s.

    • if a word ends in es, drop the s.
    • if a word ends in ing, delete the ing unless the remaining word consists only of one letter or of th.
    • If a word ends with ed, preceded by a consonant, delete the ed unless this leaves only a single letter.
    • …...
  • transform words
    • if a word ends with “ies” but not “eies” or “aies” then “ies --> y.”

Data Mining -Volinsky - 2011 - Columbia University

feature selection
Feature Selection
  • Performance of text classification algorithms can be optimized by selecting only a subset of the discriminative terms
    • Even after stemming and stopword removal.
  • Greedy search
    • Start from full set and delete one at a time
    • Find the least important variable
      • Can use Gini index for this if a classification problem
  • Often performance does not degrade even with orders of magnitude reductions
    • Chakrabarti, Chapter 5: Patent data: 9600 patents in communcation, electricity and electronics.
    • Only 140 out of 20,000 terms needed for classification!

Data Mining -Volinsky - 2011 - Columbia University

distances in td matrices
Distances in TD matrices
  • Given a term doc matrix represetnation, now we can define distances between documents (or terms!)
  • Elements of matrix can be 0,1 or term frequencies (sometimes normalized)
  • Can use Euclidean or cosine distance
  • Cosine distance is the angle between the two vectors
  • Not intuitive, but has been proven to work well
  • If docs are the same, dc =1, if nothing in common dc=0

Data Mining -Volinsky - 2011 - Columbia University

slide26

We can calculate cosine and Euclidean distance for this matrix

  • What would you want the distances to look like?

Data Mining -Volinsky - 2011 - Columbia University

document distance
Document distance
  • Pairwise distances between documents
  • Image plots of cosine distance, Euclidean, and scaled Euclidean

R function: ‘image’

Data Mining -Volinsky - 2011 - Columbia University

weighting in td space
Weighting in TD space
  • Not all phrases are of equal importance
    • E.g. David less important than Beckham
    • If a term occurs frequently in many documents it has less discriminatory power
    • One way to correct for this is inverse-document frequency (IDF).
    • Term importance = Term Frequency (TF) x IDF
    • Nj= # of docs containing the term
    • N = total # of docs
    • A term is “important” if it has a high TF and/or a high IDF.
    • TF x IDF is a common measure of term importance

Data Mining -Volinsky - 2011 - Columbia University

slide29

TF IDF

Data Mining -Volinsky - 2011 - Columbia University

queries
Queries
  • A query is a representation of the user’s information needs
    • Normally a list of words.
  • Once we have a TD matrix, queries can be represented as a vector in the same space
    • “Database Index” = (1,0,1,0,0,0)
  • Query can be a simple question in natural language
  • Calculate cosine distance between query and the TF x IDF version of the TD matrix
      • Returns a ranked vector of documents

Data Mining -Volinsky - 2011 - Columbia University

latent semantic indexing
Latent Semantic Indexing
  • Criticism: queries can be posed in many ways, but still mean the same
    • Data mining and knowledge discovery
    • Car and automobile
    • Beet and beetroot
  • Semantically, these are the same, and documents with either term are relevant.
  • Using synonym lists or thesauri are solutions, but messy and difficult.
  • Latent Semantic Indexing (LSI): tries to extract hidden semantic structure in the documents
  • Search what I meant, not what I said!

Data Mining -Volinsky - 2011 - Columbia University

slide32
LSI
  • Approximate the T-dimensional term space using principle components calculated from the TD matrix
  • The first k PC directions provide the best set of k orthogonal basis vectors - these explain the most variance in the data.
    • Data is reduced to an N x k matrix, without much loss of information
  • Each “direction” is a linear combination of the input terms, and define a clustering of “topics” in the data.
  • What does this mean for our toy example?

Data Mining -Volinsky - 2011 - Columbia University

slide34
LSI
  • Typically done using Singular Value Decomposition (SVD) to find principal components

New orthogonal basis for the data (PC directions) -

TD matrix

Diagonal matrix of eigenvalues

Term weighting by document - 10 x 6

For our example: S = (77.4,69.5,22.9,13.5,12.1,4.8)

Fraction of the variance explained (PC1&2) = = 92.5%

Data Mining -Volinsky - 2011 - Columbia University

slide35
LSI

Top 2 PC make new pseudo-terms to define documents…

Also, can look at first two Principal components:

(0.74,0.49, 0.27,0.28,0.18,0.19) -> emphasizes first two terms

(-0.28,-0.24,-0.12,0.74,0.37,0.31) -> separates the two clusters

Note how distance from the origin shows number of terms,

And angle (from the origin) shows similarity as well

Data Mining -Volinsky - 2011 - Columbia University

slide36
LSI
  • Here we show the same plot, but with two new documents, one with the term “SQL” 50 times, another with the term “Databases” 50 times.
  • Even though they have no phrases in common, they are close in LSI space

Data Mining -Volinsky - 2011 - Columbia University

textual analysis
Textual analysis
  • Once we have the data into a nice matrix representation (TD, TDxIDF, or LSI), we can throw the data mining toolbox at it:
    • Classification of documents
      • If we have training data for classes
    • Clustering of documents
      • unsupervised

Data Mining -Volinsky - 2011 - Columbia University

automatic document classification
Automatic document classification
  • Motivation
    • Automatic classification for the tremendous number of on-line text documents (Web pages, e-mails, etc.)
    • Customer comments: Requests for info, complaints, inquiries
  • A classification problem
    • Training set: Human experts generate a training data set
    • Classification: The computer system discovers the classification rules
    • Application: The discovered rules can be applied to classify new/unknown documents
  • Techniques
    • Linear/logistic regression, naïve Bayes
    • Trees not so good here due to massive dimension, few interactions

Data Mining -Volinsky - 2011 - Columbia University

na ve bayes classifier for text
Naïve Bayes Classifier for Text
  • Naïve Bayes classifier = conditional independence model
    • Also called “multivariate Bernoulli”
    • Assumes conditional independence assumption given the class: p( x | ck ) = Pp( xj | ck )
    • Note that we model each term xj as a discrete random variable

In other words, the probability that a bunch of words comes from a given class equals the product of the individual probabilities of those words.

Data Mining -Volinsky - 2011 - Columbia University

multinomial classifier for text
Multinomial Classifier for Text

.

  • Multinomial Classification model
    • Assumes that the data are generated by a p-sided die (multinomial model)
    • where Nx = number of terms (total count) in document x
    • xj = number of times term j occurs in the document
    • ck = class = k
    • Based on training data, each class has its own multinomial probability across all words.

Data Mining -Volinsky - 2011 - Columbia University

na ve bayes vs multinomial
Naïve Bayes vs. Multinomial
  • Many extensions and adaptations of both
  • Text mining classification models usually a version of one of these
  • Example: Web pages
    • Classify webpages from CS departments into:
      • student, faculty, course,project
    • Train on ~5,000 hand-labeled web pages from Cornell, Washington, U.Texas, Wisconsin
    • Crawl and classify a new site (CMU)

Data Mining -Volinsky - 2011 - Columbia University

nb vs multinomial
NB vs. multinomial

Data Mining -Volinsky - 2011 - Columbia University

slide43

Highest Probability Terms in Multinomial Distributions

Classifying web pages at a University:

Data Mining -Volinsky - 2011 - Columbia University

document clustering
Document Clustering
  • Can also do clustering, or unsupervised learning of docs.
  • Automatically group related documents based on their content.
  • Require no training sets or predetermined taxonomies.
  • Major steps
    • Preprocessing
      • Remove stop words, stem, feature extraction, lexical analysis, …
    • Hierarchical clustering
      • Compute similarities applying clustering algorithms, …
    • Slicing
      • Fan out controls, flatten the tree to desired number of levels.
  • Like all clustering examples, success is relative

Data Mining -Volinsky - 2011 - Columbia University

document clustering45
Document Clustering
  • To Cluster:
    • Can use LSI
    • Another model: Latent Dirichlet Allocation (LDA)
    • LDA is a generative probabilistic model of a corpus. Documents are represented as random mixtures over latent topics, where a topic is characterized by a distribution over words.
  • LDA:
    • Three concepts: words, topics, and documents
    • Documents are a collection of words and have a probability distribution over topics
    • Topics have a probability distribution over words
    • Fully Bayesian Model

Data Mining -Volinsky - 2011 - Columbia University

slide46
LDA
  • Assume data was generated by a generative process:
  • q is a document - made up from topics from a probability distribution
  • z is a topic made up from words from a probability distribution
  • w is a word, the only real observables (N=number of words in all documents)
  • Then, the LDA equations are specified in a fully Bayesian model:

a=per-document topic distributions

Data Mining -Volinsky - 2011 - Columbia University

slide47

Which can be solved via advance computational techniques

see Blei, et al 2003

Data Mining -Volinsky - 2011 - Columbia University

lda output
LDA output
  • The result can be an often-useful classification of documents into topics, and a distribution of each topic across words:

Data Mining -Volinsky - 2011 - Columbia University

another look at lda
Another Look at LDA
  • Model: Topics made up of words used to generate documents

Data Mining -Volinsky - 2011 - Columbia University

another look at lda50
Another Look at LDA
  • Reality: Documents observed, infer topics

Data Mining -Volinsky - 2011 - Columbia University

case study tv listings
Case Study: TV Listings
  • Use text to make recommendations for TV shows

Data Mining -Volinsky - 2011 - Columbia University

data issues
Data Issues
  • 10013|In Harm's Way|In Harm's Way|A tough Naval officer faces the enemy while fighting in the South Pacific during World War II.|A tough Naval officer faces the enemy while fighting in the South Pacific during World War II.|en-US| Movie,NR Rating|Movies:Drama|||165|1965|USA||||||STARS-3||NR|John Wayne, Kirk Douglas, Patricia Neal, Tom Tryon, Paula Prentis s, Burgess Meredith|Otto Preminger||||Otto Preminger|

Parsed Program Guide entries – 2 weeks, ~66,000 programs, 19,000 words

  • Collapse on series (syndicated shows are still a problem)
  • Stopwords/stemming, duplication, paid programming, length normalization

Data Mining -Volinsky - 2011 - Columbia University

data processing
Data Processing
  • Combine shows from one series into a ‘canonical’ format

Data Mining -Volinsky - 2011 - Columbia University

results
Results
  • We fit LDA
    • Results in a full distribution of words, topics and documents
    • Topics are unveiled which are a collection of words

Data Mining -Volinsky - 2011 - Columbia University

results55
Results
  • For user modelling, consider the collection of shows a single user watches as a ‘document’ – then look to see what topics (and hence, words) make up that document

Data Mining -Volinsky - 2011 - Columbia University

show mining via text
Show mining via text

Data Mining -Volinsky - 2011 - Columbia University

text mining helpful data
Text Mining: Helpful Data
  • WordNet

Data Mining -Volinsky - 2011 - Columbia University

Courtesy: Luca Lanzi

text mining other topics
Text Mining - Other Topics
  • Part of Speech Tagging
    • Assign grammatical tags to words (verb, noun, etc)
    • Helps in understanding documents : uses Hidden Markov Models
  • Named Entity Classification
    • Classification task: can we automatically detect proper nouns and tag them
    • “Mr. Jones” is a person; “Madison” is a town.
    • Helps with dis-ambiguation: spears

Data Mining -Volinsky - 2011 - Columbia University

text mining other topics60
Text Mining - Other Topics
  • Sentiment Analysis
    • Automatically determine tone in text: positive, negative or neutral
    • Typically uses collections of good and bad words
    • “While the traditional media is slowly starting to take John McCain’s straight talking image with increasingly large grains of salt, his base isn’t quite ready to give up on their favorite son. Jonathan Alter’s bizarre defense of McCain after he was caught telling an outright lie, perfectly captures that reluctance[.]”
    • Often fit using Naïve Bayes
  • There are sentiment word lists out there:
    • See http://neuro.imm.dtu.dk/wiki/Text_sentiment_analysis

Data Mining -Volinsky - 2011 - Columbia University

text mining other topics61
Text Mining - Other Topics
  • Summarizing text: Word Clouds
    • Takes text as input, finds the most interesting ones, and displays them graphically
    • Blogs do this
    • Wordle.net

Data Mining -Volinsky - 2011 - Columbia University

modest mouse lyrics
Modest Mouse lyrics

Data Mining -Volinsky - 2011 - Columbia University

references
References
  • Text classification
    • Excellent lecture by William Cohen – quite detailed, uses knn, TFIDF,nerural nets, other models
    • http://videolectures.net/mlas06_cohen_tc/
  • LDA and topic models
    • Seminal paper: Blei, David M.; Ng, Andrew Y.; Jordan, Michael I (January 2003). Lafferty, John. ed. "Latent Dirichlet allocation". Journal of Machine Learning Research 3
    • Tutorial on text topic modelling from David Blei: http://www.cs.princeton.edu/~blei/papers/Blei2011.pdf
  • General text mining topics
    • Many text mining tutorial available at LingPipe:
      • http://alias-i.com/lingpipe/demos/tutorial/cluster/read-me.html
    • Code available but written in Java
  • Sentiment Analysis
    • Bing Liu tutorial: http://www.cs.uic.edu/~liub/FBS/Sentiment-Analysis-tutorial-AAAI-2011.pdf
    • Searching for Bing Liu will find many resources on this topic
    • Sentiment analysis in Twitter: http://danzambonini.com/self-improving-bayesian-sentiment-analysis-for-twitter/
  • Twitter text mining tutorial
    • http://jeffreybreen.wordpress.com/2011/07/04/twitter-text-mining-r-slides/

Data Mining -Volinsky - 2011 - Columbia University