1. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 1 A Study of Text Categorization Classifying Programming Newsgroup Discussions using Text Categorization Algorithms by Lingfeng Mo

2. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 2 What is text categorization? l Definition –Classification of documents into a fixed number of predefined categories. –Sometimes alternately referred to as text data mining.

3. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 3 What is Data Mining? l Many Definitions –Non-trivial extraction of implicit, previously unknown and potentially useful information from data –Exploration & analysis, by automatic or semi-automatic means, of large quantities of data in order to discover meaningful patterns

4. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 4 Data Mining Tasks... l Classification [Predictive] l Clustering [Descriptive] l Association Rule Discovery [Descriptive] l Sequential Pattern Discovery [Descriptive] l Regression [Predictive] l Deviation Detection [Predictive]

5. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 5 Data Mining Tasks l Prediction Methods –Use some variables to predict unknown or future values of other variables. l Description Methods –Find human-interpretable patterns that describe the data. From [Fayyad, et.al.] Advances in Knowledge Discovery and Data Mining, 1996

6. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 6 Classification: Definition l Given a collection of records (training set ) –Each record contains a set of attributes, one of the attributes is the class. l Find a model for class attribute as a function of the values of other attributes. l Goal: previously unseen records should be assigned a class as accurately as possible. –A test set is used to determine the accuracy of the model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it.

7. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 7 Classification Example Test Set Training Set Model Learn Classifier Randomly choose certain portion 12 German documents 12 English documents

8. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 8 Classification Example Result

9. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 9 Why our study? l Programmer often seek and exchange information about problems on using a certain library, framework, or API online. l Titles not corresponding to the content in newsgroup discussion. l Novice doesn’t know how to ask a question exactly. l By categorizing an ongoing discussion, such techniques could be used to directly point out previous discussions of similar problems to the developers who ask questions.

10. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 10 What’s this study for? l Ideal Goal: Automatically classifying discussions into meaningful semantic categories. –Approach:  Collect and save raw data  Import and optimize data  Select certain portion of data to train a classifier model.  Classify data  Evaluate results

11. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 11 Tool we use l MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text. --a machine learning for language toolkit. l Via: http://mallet.cs.umass.edu/

12. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 12 Data Collecting l Download discussions from Java programming forum l Save each discussion as a text document(.txt) – Article(text, name) l Manually put similar discussions into the same folder (labels)

13. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 13 Import Data l Input: Labels included with their articles –How it works? l Output: Mallet document Char Sequence Token Sequenc e Feature Vectors Data Name of Article Name/ Source Label Target Name of Drive Name of Folder ++ Name of Article ｝ Instance

14. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 14 Example of Import Data l Import-file l Import-dir

15. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 15 Training Classifier l Input the produced data by importing process l Set training portion for the training set and test set l K-Fold Cross-Validation – 10 trials usually l Set the trainer - NaiveBayesTrainer

16. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 16 Basic Bayes theorem l A probabilistic framework for solving classification problems l Conditional Probability: l Bayes theorem:

17. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 17 Example of Bayes Theorem l Given: –A doctor knows that cold causes cough 50% of the time –Prior probability of any patient having cold is 1/50,000 –Prior probability of any patient having cough is 1/20 l If a patient has cough, what’s the probability he/she has cold?

18. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 18 Example of Train Classifier

19. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 19 Output of Classification l Confusion matrix l Test data accuracy for every trials l Train data accuracy mean –Standard Deviation –Standard Error l Test data accuracy mean –Standard Deviation –Standard Error

20. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 20 Exp. picture of Classification(1 of 2)

21. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 21 Exp. picture of Classification(2 of 2)

22. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 22 How to improve the accuracy? l Increase recognition rate – Words Splitting l Unify words’ tense - Words Stemming l Get rid of noisy data – Remove StopWords l About overlapped categories - Top N Method

23. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 23 Words stemming l Change Verb’s Tense back to original –Ex. Performed -> perform

24. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 24 Words Splitting (1 of 3) l In what case we could split a word? –Punctuation –Blank –Under Line

25. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 25 Words Splitting (2 of 3) l Examples: –Ex. Set_Value -> Set Value; –ImageIcon("myIcon.gif")); -> ImageIcon myIcon gif; –actionPerformed(ActionEvent e) -> actionPerformed Action Event e l See any problems? –There are some cases that people like to write many words or words with numbers together. –Ex. JButton, actionListener, Button1,2,3 and etc.

26. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 26 Words Splitting (3 of 3) l What the special cases are? –1. Begin with any number of capital letters combined with ONE or couple words. Ex. JFrame -> J Frame; JJJJJJJButton -> JJJJJJJ Button; JButtonApple -> J Button Apple –2. lower case letter/letters or lower case word combined with a word begin with capital letter Ex. cButton -> c Button; ccccccccButton -> cccccccc Button; setValue -> set Value; addActionListener -> add Action Listener; –3. Many words ALL begin with capital letter combined togeter. Ex. MyFrame-> My Frame; SetActionCommand -> Set Action Command –4. Combined with word and numbers EX. Button1 -> Button 1; 1Button -> 1 Button Button123 -> Button 123; 123Button -> 123 Button;

27. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 27 Remove Stopwords (1 Of 2) l What is stop words? –The most common, short function words, such as the, is, at, which, and, on. l Any special cases?

28. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 28 Remove Stopwords (2 Of 2) l Extra Stop Words –Programming words. Ex. public, private, class, new and etc. l Words Frequency Counter helps.

29. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 29 Overlapped Categories l Each category is treated as independent label by default. l How to solve realistic problems? – Top N Method

30. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 30 Top N Method Regular Way Top N Method

31. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 31 Some of our test results l Test base on 10 different labels and 45 instances in total. l Let’s see some pictures help us directly perceived through the senses

32. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 32 Classify data with Original Mallet Lowest: 19% Highest: 32% Average: 25.9%

33. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 33 After Stemming & Words Splitting Lowest: 26% Highest: 44% Average: 35%

34. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 34 After remove Stop Words Lowest: 36% Highest: 62% Average: 45%

35. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 35 Top N Method been used Lowest: 54% Highest: 72 % Average: 63.6%

36. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 36 Any way to improve accuracy again? l Highlight the key feature. l Use only code data as training data.

37. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 37 Only code Data Delete all the text other than code. What is considered as code? Code includes not only a snippet of code more than one line, but also a class name, such as JButton and JActionListener, or a method call, such as addActionListener(aListener).

38. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 38 Test Result of Code only Data Lowest: 24% Highest: 42% Average: 34% Lowest: 54% Highest: 72 % Average: 63.6%

39. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 39 Why this happened? l Only code data is not enough. l Can not remove too much data, especially those actually contributed to feature selection. l Is our data size not big enough?

40. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 40 Increase the Data Scale l What we done? - Increase the total instances from 45 – 158 - Increase the num of labels from 10 - 17 l Data analysis and Quality improvement since categories may overlap

41. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 41 After Data Scale Increased Lowest: 36% Highest: 62% Average: 45% Lowest: 51.88% Highest: 67.5 % Average: 59.75%

42. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 42 After Data Scale Increased with Top N Lowest: 54% Highest: 72 % Average: 63.6% Lowest: 66.25% Highest: 79.38 % Average: 72.05%

43. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 43 Why the accuracy increased? l Naïve Bayes classifier using Gaussian distribution to represent the class-conditional probability for continuous attributes, so we are wondering that if the frequency distribution of each word in the articles looks like a normal distribution l Count the frequencies of each word in the articles to create a histogram and to see whether the histogram looks like a normal distribution

44. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 44 Histogram for Word A

45. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 45 Histogram for Word B

46. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 46 Only Code Data again Lowest: 51.54 % Highest: 64.62 % Average: 58.23 % Lowest: 66.25% Highest: 79.38 % Average: 72.05%

47. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 47 Data Without Code Lowest: 67.5 % Highest: 76.25 % Average: 71.44 % Lowest: 66.25% Highest: 79.38 % Average: 72.05%

48. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 48 Results Analysis l Different from human beings, code is not the decisive factor. l Base on our prepared data, code is only a small part of a single instance.

49. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 49 Compare to Maximum Entropy Lowest: 57.5 % Highest: 70 % Average: 63.06% Lowest: 51.88% Highest: 67.5 % Average: 59.75%

50. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 50 Maximum Entropy with Top N Lowest: 69.38% Highest: 83.76 % Average: 78.63% Lowest: 66.25% Highest: 79.38 % Average: 72.05%

51. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 51 Generative and discriminative models l Generative (Joint) model -> P(c,d) Place P over both observed data and hidden stuff. - Ex. Naive Bayes l Discriminative (Conditional) models -> P(c|d) Take data as given, place a P over hidden structure given the data. -Ex. Maximum Entropy, SVMs Let’s see a picture directly.

52. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 52 Generative and discriminative models l Bayes net diagrams draw circles for random variables, and lines for direct dependencies l Some variables are observed; some are hidden l Each node (conditional model) is a little classifier based on incoming arcs C d1d1 d2d2 d3d3 c d1d1 d2d2 d3d3 Conditional modelsJoint models

53. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 53 SVM-light l SVM-light is an implementation of Support Vector Machines (SVMs) in C. l Download at http://svmlight.joachims.org/ http://svmlight.joachims.org/ l Solves classification and regression problems. solves ranking problems l Efficiently computes Leave-One-Out estimates of the error rate, the precision, and the recall. l Supports standard kernel functions and lets you define your own

54. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 54 Format of input file l The first lines may contain comments and are ignored if they start with #. Each of the following lines represents one training example and is of the following format: l.=. : :... : # l Explain.=. +1 | -1 | 0 |.=..=..=.

55. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 55 How it Works – Linear Mapping picture from http://www1.cs.columbia.edu/~kathy/cs4701/documents/jason_svm_tuto rial.pdf http://www1.cs.columbia.edu/~kathy/cs4701/documents/jason_svm_tuto rial.pdf

56. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 56 Polynomial mapping picture from http://www1.cs.columbia.edu/~kathy/cs4701/documents/jason_svm_tuto rial.pdf http://www1.cs.columbia.edu/~kathy/cs4701/documents/jason_svm_tuto rial.pdf

57. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 57 Future Work l On-going experiments, result analysis based on SVM l Continually Increase Data scale l Additional increase the identification rate on SVM. l Compare the accuracy difference between generative (Naive Bayes) and discriminative (SVM) models base on our results since we think the main reason that determines the accuracy is not the tool itself but how to select the suitable tool that best matches the data model that underlies a given text categorization problem.

58. © Lingfeng Mo Classifying Programming Newsgroup Discussions using Text Categorization Algorithms 1/19/2011 58 Reference l McCallum, Andrew Kachites. "MALLET: A Machine Learning for Language Toolkit." http://mallet.cs.umass.edu. 2002.http://mallet.cs.umass.edu l Pang-Ning Tan, Michael Steinbach, and Vipin Kumar. Introduction to Data Mining, 2005 l Lecture 7 of Artificial Intelligence | Natural Language Processing Course at Stanford. Instructor: Manning, Christopher D. l K. Nigam, J. Lafferty, and A. Mccallum. Using maximum entropy for text classification. In IJCAI-99 Workshop on Machine Learning for Information Filtering, 1999. l T. Joachims, Making large-Scale SVM Learning Practical. Advances in Kernel Methods - Support Vector Learning, B. Schölkopf and C. Burges and A. Smola (ed.), MIT-Press, 1999. l T. Joachims. Text categorization with support vector machines: Learning with many relevant features. University at Dortmund, LS VIII, 1997.