from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import classification_report
from sklearn.svm import SVC
 
training_corpus = [
    ("I am exhausted of this work", "class_B"),
    ("I can't cooperate with this", "class_B"),
    ("He is my badest enemy", "class_B"),
    ("My management is poor", "class_B"),
    ("I love this burger", "class_A"),
    ("This is an brilliant place!", "class_A"),
    ("I feel very good about these dates", "class_A"),
    ("This is my best work", "class_A"),
    ("What an awesome view", "class_A"),
    ("I do not like this dish", "class_B")
]
 
test_corpus = [
    ("I am not feeling well, today", "class_B"),
    ("I feel brilliant", "class_A"),
    ("Gray is a friend of mine", "class_A"),
    ("I do not enjoy my job", "class_B")
]
 
train_data = []
train_labels = []
for row in training_corpus:
    train_data.append(row[0])
    train_labels.append(row[1])
 
test_data = []
test_labels = []
for row in test_corpus:
    test_data.append(row[0])
    test_labels.append(row[1])
 
vectorizer = TfidfVectorizer(min_df=4, max_df=0.9)
train_vectors = vectorizer.fit_transform(train_data)
test_vectors = vectorizer.transform(test_data)
 
model = SVC(kernel="linear")
model.fit(train_vectors, train_labels)
 
prediction = model.predict(test_vectors)
 
print(prediction)
print(classification_report(test_labels, prediction))
 

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