Bygga en BoW Naive Bayes-klassificerare

Funktionsutveckling för NLP i Python

Rounak Banik

Data Scientist

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Steg

Textförbehandling
Bygga en bag-of-words-modell (eller representation)
Maskininlärning

Textförbehandling med CountVectorizer

Argument för CountVectorizer

lowercase: False, True
strip_accents: 'unciode', 'ascii', None
stop_words: 'english', list, None
token_pattern: regex
tokenizer: function

Bygga BoW-modellen

# Import CountVectorizer
from sklearn.feature_extraction.text import CountVectorizer


# Create CountVectorizer object
vectorizer = CountVectorizer(strip_accents='ascii', stop_words='english', lowercase=False)


# Import train_test_split
from sklearn.model_selection import train_test_split

# Split into training and test sets
X_train, X_test, y_train, y_test = train_test_split(df['message'], df['label'], test_size=0.25)

Bygga BoW-modellen

...
...
# Generate training Bow vectors
X_train_bow = vectorizer.fit_transform(X_train)


# Generate test BoW vectors
X_test_bow = vectorizer.transform(X_test)

Träna Naive Bayes-klassificeraren

# Import MultinomialNB
from sklearn.naive_bayes import MultinomialNB


# Create MultinomialNB object
clf = MultinomialNB()


# Train clf
clf.fit(X_train_bow, y_train)


# Compute accuracy on test set
accuracy = clf.score(X_test_bow, y_test)
print(accuracy)

0.760051

Nu kör vi en övning!

Funktionsutveckling för NLP i Python