Sentiment Analysis how to get the probability of the result?

Question

So i have this simple sentiment analysis app here

so far i can only print its result in Positive/negative/neutral but i want it to also print it's probability on why it's a Positive sentence.

like this one

 Positive 
 85.2%

Can anyone help me?

Here's my code

import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import MultinomialNB
from sklearn.preprocessing import StandardScaler
import re
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from sklearn.utils import shuffle
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix,f1_score,precision_score,recall_score
wordnet_lemmatizer = WordNetLemmatizer()

df = pd.read_csv('Tweets.csv')

def normalizer(comment):
      only_letters = re.sub("[^a-zA-Z]", " ", comment)
      only_letters = only_letters.lower()
      only_letters = only_letters.split()
      filtered_result = [word for word in only_letters if word not in stopwords.words('english')]
      lemmas = [wordnet_lemmatizer.lemmatize(t) for t in filtered_result]
      lemmas = ' '.join(lemmas)
      return lemmas

df = shuffle(df)
y = df['airline_sentiment']
x = df.text.apply(normalizer)

vectorizer = CountVectorizer()
x_vectorized = vectorizer.fit_transform(x)

train_x,val_x,train_y,val_y = train_test_split(x_vectorized,y)


regressor = LogisticRegression(multi_class='multinomial', solver='newton-cg')
model = regressor.fit(train_x, train_y)

params = {'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000] }
gs_clf = GridSearchCV(model, params, n_jobs=1, cv=5)
gs_clf = gs_clf.fit(train_x, train_y)
model = gs_clf.best_estimator_

#_f1 = f1_score(val_y, y_pred, average='micro')
#_confusion = confusion_matrix(val_y, y_pred)
#__precision = precision_score(val_y, y_pred, average='micro')
#_recall = recall_score(val_y, y_pred, average='micro')
#_statistics = {'f1_score': _f1,
#              'confusion_matrix': _confusion,
#              'precision': __precision,
#              'recall': _recall
#              }

y_pred = model.predict(val_x)
print(accuracy_score(val_y, y_pred))

test_feature = vectorizer.transform(['The Movie is good'])
print(model.predict(test_feature,))

and my current output is:

0.7846994535519126
['positive']

Answer 1

prediction_probablities = model.predict_proba(val_x)

for additional information:

https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html