NlpTools

Sentiment detection Mar 30th, 2013

We will be doing a bit of sentiment detection using NlpTools and aim at recreating the results of a popular sentiment classification paper of Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan, "Thumbs up? Sentiment Classification using Machine Learning Techniques"

Getting and preparing the dataset

The dataset that we will be using is the polarity dataset v2.0 found here. The dataset is a collection of movie reviews from imdb, already labeled and tokenized. NlpTools does not have any tools for model evaluation so we will have to code our own manually for this mini nlp project.

We will use 10-fold cross-validation. We will be creating lists of files split for training and evaluation. I am assuming that you are using either Linux or Mac, if that is not the case, really sorry, you can either download the lists from here and replace _DIR_ with the path to the files or write a script to create them on your own.

First we will create a list of all the files and shuffle it. Run the following commands in a terminal, one directory above the neg and pos directories.

ls neg/ | xargs -I file echo `pwd`/neg/file >> /tmp/imdb.list
ls pos/ | xargs -I file echo `pwd`/pos/file >> /tmp/imdb.list
shuf /tmp/imdb.list >/tmp/imdb-shuffled.list
for i in {200..2000..200};do p=$(($i/200)); head -n $i /tmp/imdb-shuffled.list | tail -n 200 >test_$p ;done
for i in {200..2000..200};do p=$(($i/200)); s=$(($i-200)); e=$((2000-$i)); head -n $s /tmp/imdb-shuffled.list >train_$p; tail -n $e /tmp/imdb-shuffled.list >>train_$p; done
 

We now have 20 files named test_*, train_* that contain lists to the documents to be used for each fold. We will next code a function to create a training set from a list of documents.

use NlpTools\Tokenizers\WhitespaceTokenizer;
use NlpTools\Documents\TokensDocument;
use NlpTools\Documents\TrainingSet;
 
function create_training_set($fname) {
    $tset = new TrainingSet();
    $tok = new WhitespaceTokenizer();
    $subs = function ($s) { return substr($s,0,-1); };
    array_walk(
        array_filter( // filter empty lines (''==false)
            array_map( // map $subs to remove new line from the end
                $subs,
                file($fname)
            )
        ),
        function ($file) use($tset,$tok) {
            $tset->addDocument(
                (strpos($file,'pos')===FALSE) ? 'neg' : 'pos',
                new TokensDocument(
                    $tok->tokenize(
                        file_get_contents($file)
                    )
                )
            );
        }
    );
    return $tset;
}
 

Classification

In this post we will train a Naive Bayes model without removing stopwords and/or infrequent words as done by Pang, Lee. We will be using frequency of unigrams.

Let's set things up.

include('vendor/autoload.php');
include('create_training_set.php');
 
$train = $argv[1]; // the first parameter is the training set list file
$test = $argv[2]; // the second parameter is the test set list file
 
// ... check if the files are ok ...
 
// use the function we coded above to create the sets of documents
$training_set = create_training_set($train);
$test_set = create_test_set($test);
 

Now that we have created the training/test sets we must train our model. Firstly we will decide what features to use and whether we will create our own feature factory or use one provided by NlpTools. Since we want to simply use as features the frequency of unigrams we can use DataAsFeatures.

Let's train our model.

$feature_factory = new DataAsFeatures();
$model = new FeatureBasedNB();
$model->train($feature_factory, $training_set);
 

Now we should evaluate our trained model on the test set. We will use the simple measure of accuracy (correct/total) as used by Pang, Lee in their paper.

// counter of correct predictions
$correct = 0;
// the classifier
$cls = new MultinomialNBClassifier($feature_factory, $model);
$classes = $test_set->getClassSet();
foreach ($test_set as $class=>$doc) {
    // predict a class for this doc
    $prediction = $cls->classify(
        $classes,
        $doc
    );
    // if correct add one to the counter
    $correct += (int)($prediction==$class);
}
 
echo ((float)$correct)/count($test_set), PHP_EOL;
 

Now to run the script, if we have saved the above 3 snippets in a file named nb.php, we can simply write the following in a terminal.

php nb.php path/to/train_1 path/to/test_1
 

And to run the crossvalidation (replace the "data/" with the path that train_* is in).

echo "(`for i in {1..10}; do php nb.php data/train_$i data/test_$i; done | paste -sd+`)*10" | bc
 

Using these lists (you need to replace _DIR_ with the path to the files), my model has an accuracy of 81.85%, which is actually quite better than 78.7 percent that was achieved by 3-fold crossvalidation in Pang and Lee 's paper.

Maxent

In the next post on this topic we will train a maximum entropy model and see if we can push this 81.85% any further.