Sentiment analysis with nltk
0
$begingroup$
I'm studying sentimental analysis with python library nltk, following this example:
dataset = pd.read_csv('Restaurant_Reviews.tsv', delimiter = 't', quoting = 3)
for i in range(0, 1000):
review = re.sub('[^a-zA-Z]', ' ', dataset['Review'][i])
review = review.lower()
review = review.split()
ps = PorterStemmer()
review = [ps.stem(word) for word in review if not word in set(stopwords.words('english'))]
review = ' '.join(review)
corpus.append(review)
cv = CountVectorizer(max_features = 1500)
X = cv.fit_transform(corpus).toarray()
y = dataset.iloc[:, 1].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)
classifier = GaussianNB()
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
I'm wondering how to classify a new review after having trained the classifier. I mean, if I had "Delicious!!", how to put it in the classifier to have 0 or 1 as result?
python nlp sentiment-analysis nltk
asked 17 hours ago
Bruce WayneBruce Wayne
1
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Bruce Wayne is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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0
$begingroup$
I'm studying sentimental analysis with python library nltk, following this example:
dataset = pd.read_csv('Restaurant_Reviews.tsv', delimiter = 't', quoting = 3)
for i in range(0, 1000):
review = re.sub('[^a-zA-Z]', ' ', dataset['Review'][i])
review = review.lower()
review = review.split()
ps = PorterStemmer()
review = [ps.stem(word) for word in review if not word in set(stopwords.words('english'))]
review = ' '.join(review)
corpus.append(review)
cv = CountVectorizer(max_features = 1500)
X = cv.fit_transform(corpus).toarray()
y = dataset.iloc[:, 1].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)
classifier = GaussianNB()
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
I'm wondering how to classify a new review after having trained the classifier. I mean, if I had "Delicious!!", how to put it in the classifier to have 0 or 1 as result?
python nlp sentiment-analysis nltk
asked 17 hours ago
Bruce WayneBruce Wayne
1
New contributor
Bruce Wayne is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
0
0
0
$begingroup$
I'm studying sentimental analysis with python library nltk, following this example:
dataset = pd.read_csv('Restaurant_Reviews.tsv', delimiter = 't', quoting = 3)
for i in range(0, 1000):
review = re.sub('[^a-zA-Z]', ' ', dataset['Review'][i])
review = review.lower()
review = review.split()
ps = PorterStemmer()
review = [ps.stem(word) for word in review if not word in set(stopwords.words('english'))]
review = ' '.join(review)
corpus.append(review)
cv = CountVectorizer(max_features = 1500)
X = cv.fit_transform(corpus).toarray()
y = dataset.iloc[:, 1].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)
classifier = GaussianNB()
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
I'm wondering how to classify a new review after having trained the classifier. I mean, if I had "Delicious!!", how to put it in the classifier to have 0 or 1 as result?
python nlp sentiment-analysis nltk
asked 17 hours ago
Bruce WayneBruce Wayne
1
New contributor
Bruce Wayne is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I'm studying sentimental analysis with python library nltk, following this example:
dataset = pd.read_csv('Restaurant_Reviews.tsv', delimiter = 't', quoting = 3)
for i in range(0, 1000):
review = re.sub('[^a-zA-Z]', ' ', dataset['Review'][i])
review = review.lower()
review = review.split()
ps = PorterStemmer()
review = [ps.stem(word) for word in review if not word in set(stopwords.words('english'))]
review = ' '.join(review)
corpus.append(review)
cv = CountVectorizer(max_features = 1500)
X = cv.fit_transform(corpus).toarray()
y = dataset.iloc[:, 1].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)
classifier = GaussianNB()
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
I'm wondering how to classify a new review after having trained the classifier. I mean, if I had "Delicious!!", how to put it in the classifier to have 0 or 1 as result?
python nlp sentiment-analysis nltk
python nlp sentiment-analysis nltk
asked 17 hours ago
Bruce WayneBruce Wayne
1
New contributor
Bruce Wayne is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 17 hours ago
Bruce WayneBruce Wayne
1
New contributor
Bruce Wayne is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 17 hours ago
Bruce WayneBruce Wayne
1
New contributor
Bruce Wayne is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 17 hours ago
Bruce WayneBruce Wayne
1
asked 17 hours ago
Bruce WayneBruce Wayne
1
1
New contributor
Bruce Wayne is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Bruce Wayne is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Bruce Wayne is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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