Classifica(on and Clustering with WEKA. Classifica*on and Clustering with WEKA

Transcription

1 Classifica(on and Clustering with WEKA 1

2 Schedule: Classifica(on and Clustering with WEKA 1. Presentation of WEKA. 2. Your turn: perform classification and clustering. 2

3 WEKA Weka is a collec*on of machine learning algorithms for data mining tasks. Weka can be used with GUI, command line and directly inside your java code. hfp:// We are interested in performing Clustering and Classifica*on. 3

4 Dataset Input file format Weka reads data in arff file (AFribute- Rela*on File Format). An arff file is a text file. This type of file is divided horizontally in 3 parts: 1) dataset name. 2) list of all the afributes in the dataset. 3) list of all elements in the dataset. To use WEKA we must to represent our training- set in a.arff file. 4

5 Dataset Input file format TRAINING- example hair { long, short, bald height weight necklace { TRUE, FALSE genre { male, female short, 1.80, 75, FALSE, female long, 1.85, 90, FALSE, male bald, 1.75,?, FALSE, male long, 1.72, 64, TRUE, female? represents missing value. use % to comment an en*re line. The last a>ribute MUST be the a>ribute that has to be predicted by the classifier. Of course, in general, we can have more than two classes. 5

6 Problem We want to develop a classifier for movie reviews. Given a review in input the classifier has to be able to classify the review as posi,ve or nega,ve. As training- set we can use the following dataset: hfp:// review- data/review_polarity.tar.gz. This dataset contains 1000 posi*ve reviews and 1000 nega*ve reviews from IMDB. Thumbs up? Sentiment Classification using Machine Learning Techniques. Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan. Proceedings of EMNLP, pp ,

7 Installing WEKA Download from hfp:// the Stable book 3rd ed. version. Set the CLASSPATH: Modify the content of the file set- my- classpath_weka.sh replacing "weka_jar_file_directory" with the path of the folder containing the weka.jar file. 7

8 Conver(ng the dataset Unzip the training- set downloaded from: hfp:// review- data/ review_polarity.tar.gz. Convert the downloaded dataset in a.arff file using weka tools: java weka.core.converters.textdirectoryloader dir./review_polarity/txt_sentoken/ >./ training_set.arff Now we have our training set in a weka suitable format ;) Have a look inside the created file training_set.arff, to see how reviews have been converted. How many types of afributes are present in the file? 8

9 Open WEKA. Click on Explorer Run WEKA Click on Preprocess Click on Open File and select training_set.arff. Choose as filter weka/filters/unsupervised/afributes/ StringToWordVector. Click on the white text- box containing now StringToWordVector... to setup the filter. Try IDFTransform=true and TFTransform=true Try Snowball stemmer. Try to use a stopwords list. Apply the filter ;) 9

10 Click on Classify Select a>ribute to use as the class. Select NaiveBayes as classifier. Select Cross- valida*on Folds=10 Start! Select the Classifier Look at the performance... Now try a knn classifier: the IBk classifier. Click on the white text- box containing now IBk... to setup the classifier. Start Compare the performance of the two classifiers J 10

11 Clustering Click on Cluster Choose SimpleKMeans as clustering algorithm. Click on the white text- box containing now SimpleKMeans... to set K=10. Select Use training set Select Store Clusters for visualiza*on Click on Ignore afributes and select Start! Have a look at the results... 11

12 The objec(ve is to obtain, for each input instance, the associated cluster. Clustering output We will obtain a new.arff file with a new a>ribute that represents the associated cluster: Cluster. 12