Created
February 10, 2022 08:22
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Step 1: Build a pandas model for sentiment classification
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import numpy as np | |
import pandas as pd | |
from sklearn.feature_extraction.text import CountVectorizer | |
from sklearn.linear_model import LogisticRegression | |
from sklearn.metrics import classification_report, roc_auc_score, roc_curve | |
from sklearn.pipeline import Pipeline | |
%%bash | |
if [ ! -f ./trainingandtestdata.zip ]; then | |
wget -q http://cs.stanford.edu/people/alecmgo/trainingandtestdata.zip | |
unzip -n trainingandtestdata.zip | |
fi | |
columns = ['polarity', 'tweetid', 'date', 'query_name', 'user', 'text'] | |
dftrain = pd.read_csv('training.1600000.processed.noemoticon.csv', | |
header = None, | |
encoding ='ISO-8859-1') | |
dftest = pd.read_csv('testdata.manual.2009.06.14.csv', | |
header = None, | |
encoding ='ISO-8859-1') | |
dftrain.columns = columns | |
dftest.columns = columns | |
sentiment_lr = Pipeline([ | |
('count_vect', CountVectorizer(min_df = 100, | |
stop_words = 'english')), | |
('lr', LogisticRegression())]) | |
sentiment_lr.fit(dftrain.text, dftrain.polarity) | |
Xtest, ytest = dftest.text[dftest.polarity!=2], dftest.polarity[dftest.polarity!=2] | |
print(classification_report(ytest,sentiment_lr.predict(Xtest))) | |
sentiment_lr.predict(Xtest).shape |
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