Implementing Text Classification in Python

vujennyfer

Hello,

I am trying to use Python (or if you could recommend a better tool) to determine if a review is "negative" or "positive". I have tried the Naive Bayes classifier tool but it needs at least 2 fields to train the model. If there is a way to truly get the model to learn text classification (NLP), that would be very helpful! I've attached a training set labeling if a review is negative or positive, and a test set that would see if the model could determine if it was "negative" or "positive". 

Training set: 

Test set (desired outcome is the review type being predicted):

dummy_data_python.xlsx

python_text_classification_dummy_data.yxmd

AkimasaKajitani

Hi @vujennyfer 

I made the workflow for you.

Python code is as follows.

from sklearn.naive_bayes import MultinomialNB
from sklearn.feature_extraction.text import CountVectorizer

#Read from AlteryxData
df = Alteryx.read("#1")
dfp = Alteryx.read("#2")

#Make Model
clf = MultinomialNB()
vec = CountVectorizer(min_df=1)
X = vec.fit_transform(df['review'])
clf.fit(X, df['review_type'])

#Predict
X_p = vec.transform(dfp['review'])
predicted = clf.predict(X_p)

# Output the list of files as dataframe
dfp['review_type_predicted'] = predicted
Alteryx.write(dfp,1)

 Result:

python_text_classification_dummy_data_AK.yxzp