Statistical Machine Learning for Text Classification with scikit-learn and NLTK
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Statistical Machine Learning for Text Classification with scikit-learn and NLTK
- 1. Statistical Learning for Text Classification with scikit-learn and NLTK Olivier Grisel http://twitter.com/ogrisel PyCon – 2011
- 2. Outline ● Why text classification? ● What is text classification? ● How? ● scikit-learn ● NLTK ● Google Prediction API ● Some results
- 3. Applications of Text Classification Task Predicted outcome Spam filtering Spam, Ham, Priority Language guessing English, Spanish, French, ... Sentiment Analysis for Product Positive, Neutral, Negative Reviews News Feed Topic Politics, Business, Technology, Categorization Sports, ... Pay-per-click optimal ads Will yield clicks / Won't placement Recommender systems Will I buy this book? / I won't
- 4. Supervised Learning Overview ● Convert training data to a set of vectors of features ● Build a model based on the statistical properties of features in the training set, e.g. ● Naïve Bayesian Classifier ● Logistic Regression ● Support Vector Machines ● For each new text document to classify ● Extract features ● Asked model to predict the most likely outcome
- 5. Summary Training features Text vectors Documents, Images, Sounds... Machine Learning Algorithm Labels New Text features Document, vector Predictive Expected Image, Model Label Sound
- 6. Bags of Words ● Tokenize document: list of uni-grams ['the', 'quick', 'brown', 'fox', 'jumps', 'over', 'the', 'lazy', 'dog'] ● Binary occurrences / counts: {'the': True, 'quick': True...} ● Frequencies: {'the': 0.22, 'quick': 0.11, 'brown': 0.11, 'fox': 0.11…} ● TF-IDF {'the': 0.001, 'quick': 0.05, 'brown': 0.06, 'fox': 0.24…}
- 7. Better than frequencies: TF-IDF ● Term Frequency ● Inverse Document Frequency ● Non informative words such as “the” are scaled done
- 8. Even better features ● bi-grams of words: ● “New York”, “very bad”, “not good” ● n-grams of chars: ● “the”, “ed ”, “ a ” (useful for language guessing) ● Combine with: ● Binary occurrences ● Frequencies ● TF-IDF
- 9. scikit-learn
- 10. scikit-learn ● BSD ● numpy / scipy / cython / c++ wrappers ● Many state of the art implementations ● A new release every 3 months ● 17 contributors on release 0.7 ● Not just for text classification
- 11. Features Extraction in scikit-learn from scikits.learn.features.text import WordNGramAnalyzer text = (u"J'ai mangxe9 du kangourou ce midi," u" c'xe9tait pas trxeas bon.") WordNGramAnalyzer(min_n=1, max_n=2).analyze(text) [u'ai', u'mange', u'du', u'kangourou', u'ce', u'midi', u'etait', u'pas', u'tres', u'bon', u'ai mange', u'mange du', u'du kangourou', u'kangourou ce', u'ce midi', u'midi etait', u'etait pas', u'pas tres', u'tres bon']
- 12. Features Extraction in scikit-learn from scikits.learn.features.text import CharNGramAnalyzer analyzer = CharNGramAnalyzer(min_n=3, max_n=6) char_ngrams = analyzer.analyze(text) print char_ngrams[:5] + char_ngrams[-5:] [u"j'a", u"'ai", u'ai ', u'i m', u' ma', u's tres', u' tres ', u'tres b', u'res bo', u'es bon']
- 13. TF-IDF features & SVMs from scikits.learn.features.text.sparse import Vectorizer from scikits.learn.sparse.svm.sparse import LinearSVC vec = Vectorizer(analyzer=analyzer) features = vec.fit_transform(list_of_documents) clf = LinearSVC(C=100).fit(features, labels) clf2 = pickle.loads(pickle.dumps(clf)) predicted_labels = clf2.predict(features_of_new_docs)
- 14. ) cs (do form ns .tra Training features Text c Documents, ve vectors Images, Sounds... ) Machine X,y fit( Learning . clf Algorithm Labels w) _ ne w) cs _n e (do X m ct( sfor ed i New an . pr Text c.t r clf e features Document, v vector Predictive Expected Image, Model Label Sound
- 15. NLTK ● Code: ASL 2.0 & Book: CC-BY-NC-ND ● Tokenizers, Stemmers, Parsers, Classifiers, Clusterers, Corpus Readers
- 16. NLTK Corpus Downloader >>> import nltk >>> nltk.download()
- 17. Using a NLTK corpus >>> from nltk.corpus import movie_reviews as reviews >>> pos_ids = reviews.fileids('pos') >>> neg_ids = reviews.fileids('neg') >>> len(pos_ids), len(neg_ids) 1000, 1000 >>> reviews.words(pos_ids[0]) ['films', 'adapted', 'from', 'comic', 'books', 'have', ...]
- 18. Common data cleanup operations ● Lower case & remove accentuated chars: import unicodedata s = ''.join(c for c in unicodedata.normalize('NFD', s.lower()) if unicodedata.category(c) != 'Mn') ● Extract only word tokens of at least 2 chars ● Using NLTK tokenizers & stemmers ● Using a simple regexp: re.compile(r"bww+b", re.U).findall(s)
- 19. Feature Extraction with NLTK Unigram features def word_features(words): return dict((word, True) for word in words)
- 20. Feature Extraction with NLTK Bigram Collocations from nltk.collocations import BigramCollocationFinder from nltk.metrics import BigramAssocMeasures as BAM from itertools import chain def bigram_features(words, score_fn=BAM.chi_sq): bg_finder = BigramCollocationFinder.from_words(words) bigrams = bg_finder.nbest(score_fn, 100000) return dict((bg, True) for bg in chain(words, bigrams))
- 21. The NLTK Naïve Bayes Classifier from nltk.classify import NaiveBayesClassifier neg_examples = [(features(reviews.words(i)), 'neg') for i in neg_ids] pos_examples = [(features(reviews.words(i)), 'pos') for i in pos_ids] train_set = pos_examples + neg_examples classifier = NaiveBayesClassifier.train(train_set)
- 22. Most informative features >>> classifier.show_most_informative_features() magnificent = True pos : neg = 15.0 : 1.0 outstanding = True pos : neg = 13.6 : 1.0 insulting = True neg : pos = 13.0 : 1.0 vulnerable = True pos : neg = 12.3 : 1.0 ludicrous = True neg : pos = 11.8 : 1.0 avoids = True pos : neg = 11.7 : 1.0 uninvolving = True neg : pos = 11.7 : 1.0 astounding = True pos : neg = 10.3 : 1.0 fascination = True pos : neg = 10.3 : 1.0 idiotic = True neg : pos = 9.8 : 1.0
- 23. Training NLTK classifiers ● Try nltk-trainer ● python train_classifier.py --instances paras --classifier NaiveBayes –bigrams --min_score 3 movie_reviews
- 24. REST services
- 25. NLTK – Online demos
- 26. NLTK – REST APIs % curl -d "text=Inception is the best movie ever" http://text-processing.com/api/sentiment/ { "probability": { "neg": 0.36647424288117808, "pos": 0.63352575711882186 }, "label": "pos" }
- 30. Typical performance results: movie reviews ● nltk: ● unigram occurrences ● Naïve Bayesian Classifier ~ 70% ● Google Prediction API ~ 83% ● scikit-learn: ● TF-IDF unigram features ● LinearSVC ~ 87% ● nltk: ● Collocation features selection ● Naïve Bayesian Classifier ~ 97%
- 31. Typical results: newsgroups topics classification ● 20 newsgroups dataset ● ~ 19K short text documents ● 20 categories ● By date train / test split ● Bigram TF-IDF + LinearSVC: ~ 87%
- 32. Confusion Matrix (20 newsgroups) 00 alt.atheism 01 comp.graphics 02 comp.os.ms-windows.misc 03 comp.sys.ibm.pc.hardware 04 comp.sys.mac.hardware 05 comp.windows.x 06 misc.forsale 07 rec.autos 08 rec.motorcycles 09 rec.sport.baseball 10 rec.sport.hockey 11 sci.crypt 12 sci.electronics 13 sci.med 14 sci.space 15 soc.religion.christian 16 talk.politics.guns 17 talk.politics.mideast 18 talk.politics.misc 19 talk.religion.misc
- 33. Typical results: Language Identification ● 15 Wikipedia articles ● [p.text_content() for p in html_tree.findall('//p')] ● CharNGramAnalyzer(min_n=1, max_n=3) ● TF-IDF ● LinearSVC
- 35. Scaling to many possible outcomes ● Example: possible outcomes are all the categories of Wikipedia (565,108) ● From Document Categorization to Information Retrieval ● Fulltext index for TF-IDF similarity queries ● Smart way to find the top 30 search keywords ● Use Apache Lucene / Solr MoreLikeThisQuery
- 36. Some pointers ● http://scikit-learn.sf.net doc & examples http://github.com/scikit-learn code ● http://www.nltk.org code & doc & PDF book ● http://streamhacker.com/ ● Jacob Perkins' blog on NLTK & APIs ● https://github.com/japerk/nltk-trainer ● http://www.slideshare.net/ogrisel these slides ● http://twitter.com/ogrisel / http://github.com/ogrisel Questions?