Difference between revisions of "Text Mining: Sentiment Classifier"
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* SentiWordNet http://sentiwordnet.isti.cnr.it | * SentiWordNet http://sentiwordnet.isti.cnr.it | ||
| − | + | ==Cara Install== | |
| − | + | Perintah shell | |
| − | python setup.py install | + | python setup.py install |
| − | + | ==Dokumen== | |
| − | + | ||
| − | + | http://readthedocs.org/docs/sentiment_classifier/en/latest/ | |
Try Online | Try Online | ||
| − | + | ==Penggunaan== | |
| − | + | Perintah shell | |
senti_classifier -c file/with/review.txt | senti_classifier -c file/with/review.txt | ||
| − | Python | + | ==Penggunaan Python== |
| − | + | Perintah shell | |
cd sentiment_classifier/src/senti_classifier/ | cd sentiment_classifier/src/senti_classifier/ | ||
python senti_classifier.py -c reviews.txt | python senti_classifier.py -c reviews.txt | ||
| − | Library | + | ==Penggunaan Library== |
from senti_classifier import senti_classifier | from senti_classifier import senti_classifier | ||
| Line 39: | Line 39: | ||
pos_score, neg_score = senti_classifier.polarity_scores(sentences) | pos_score, neg_score = senti_classifier.polarity_scores(sentences) | ||
print pos_score, neg_score | print pos_score, neg_score | ||
| − | |||
| − | |||
| − | |||
Revision as of 09:21, 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
Cara Install
Perintah shell
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