Difference between revisions of "Text Mining: Sentiment Classifier"
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* NumPy http://numpy.scipy.org | * NumPy http://numpy.scipy.org | ||
* SentiWordNet http://sentiwordnet.isti.cnr.it | * SentiWordNet http://sentiwordnet.isti.cnr.it | ||
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+ | Jika belum di instalasi, tampaknya sentiment classifer akan men-download & meng-compile semua yang dibutuhkan. | ||
==Cara Install== | ==Cara Install== |
Revision as of 09:32, 3 February 2017
Sentiment Classifier menggunakan Word Sense Disambiguation menggunakan WordNet dan statistik terjadinya kata dari corpus movie review NLTK. Mengklasifikasikan ke dalam kategori positif dan negatif.
Persyaratan
- Python 2.6.
- NLTK http://www.nltk.org 2.0
- NumPy http://numpy.scipy.org
- SentiWordNet http://sentiwordnet.isti.cnr.it
Jika belum di instalasi, tampaknya sentiment classifer akan men-download & meng-compile semua yang dibutuhkan.
Cara Install
Akan butuh akses ke folder
/usr/local/lib/python2.7/dist-packages/
Perintah shell
sudo python setup.py install
Dokumen
http://readthedocs.org/docs/sentiment_classifier/en/latest/ Try Online
Penggunaan
Perintah shell
senti_classifier -c file/with/review.txt
Penggunaan Python
Perintah shell
cd sentiment_classifier/src/senti_classifier/ python senti_classifier.py -c reviews.txt
Penggunaan Library
from senti_classifier import senti_classifier sentences = ['The movie was the worst movie', 'It was the worst acting by the actors'] pos_score, neg_score = senti_classifier.polarity_scores(sentences) print pos_score, neg_score