Awesome
Analisis Sentimen Twitter dengan TFIDF-ANN
Sentimen analisis twit dari Twitter untuk menentukan apakah sebuah twitter dianggap berbau POSITIF atau NEGATIF. Sentimen analisis ini menggunakan Multilayer Perceptron dengan ekstraksi fitur TF-IDF (Term Frequency and Inverse Document Frequency)
Installation
scikit-learn, nltk, numpy, keras (dan backendnya), csv
Usage
# analiser.py
# main class: Analiser(training_data)
# training_data default value = 'data/training_all_random.csv'
#
# for main class example see main_*.py file, try run the file
#
# --
# main_pre_trained.py | load existing model
# analiser load training_data as base train data, load existing model
an = Analiser(training_data='data/training_all_random.csv')
an.load_model(filename='model')
# --
# main_training.py | train new model
# analiser load training_data as base train data, train the data, save the model
an = Analiser(training_data='data/training_all_random.csv')
an.train(filename='model')
# --
# main_re_training.py | retrain existing model
# analiser load training_data as base train data, load existing model, train the data, save the model
an = Analiser(training_data='data/training_all_random.csv')
an.retrain(filename='model')
Testing
Pada data diatas, dataset yang digunakan adalah twit mengenai pilkada DKI Jakarta kemarin.
# let analiser_instance is an instance of Analiser
test = "ahok itu pemimpin yang beres memimpin"
print test
print analiser_instance.testFromTrained([analiser_instance.tfidf_data.transform(test)])
# output: POSITIF
test = "ahok itu pemimpin yang ga beres memimpin"
print test
print analiser_instance.testFromTrained([analiser_instance.tfidf_data.transform(test)])
# output: NEGATIF
Train / Re-train Network Forward/Backprop Parameters
# analiser.py
# Change parameter for training
def train(self, output_filename = 'model'):
...
learning_rate = .01
loss_error = 'binary_crossentropy'
batch_size = 1
epoch = 10
...
# analiser.py
# Change parameter for retraining
def retrain(self, output_filename):
...
learning_rate = .005
loss_error = 'binary_crossentropy'
batch_size = 1
epoch = 3
...
Contributing
- Fork it!
- Create your feature branch:
git checkout -b my-new-feature - Commit your changes:
git commit -am 'Add some feature' - Push to the branch:
git push origin my-new-feature - Submit a pull request :D
Credits
Dataset, koleksi barang-barang bahasa Indonesia, dan beberapa bagian preproses
https://github.com/ramaprakoso/analisis-sentimen
TF-IDF inspiration