Python Text Analysis with TextBlob — Reading lines from The Office TV Show
$begingroup$
I have a Jupyter notebook that allows a user to type in a phrase and the program will 'guess' which Office character it resembles.
Here is a link to the github repo
I have the following littered throughout my code:
the_office_raw_script['polarity'] = the_office_raw_script['line_text'].apply(lambda x: TextBlob(x).sentiment.polarity)
the_office_raw_script['scores'] = the_office_raw_script['line_text'].apply(lambda x: analyser.polarity_scores(x))
and the following, which is the prediction part:
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.naive_bayes import MultinomialNB
X_train, X_test, y_train, y_test = train_test_split(df_upsampled['line_text'], df_upsampled['speaker'], random_state = 0)
count_vect = CountVectorizer()
X_train_counts = count_vect.fit_transform(X_train)
tfidf_transformer = TfidfTransformer()
X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)
clf = MultinomialNB().fit(X_train_tfidf, y_train)
I have some examples of output, which is AWESOME! (so happy i got this far)
print(clf.predict(count_vect.transform(["hard working beet farmer"])))
the above prints dwight
print(clf.predict(count_vect.transform(["that's what she said"])))
the above prints pam which is totally wrong because michael says 'that's what she said' over 15 times throughout the series.
Two Questions
How would I go above making my script into a function, would that be worth it?
How do I somewhat ~hardcode~ some inputs? If someone types in 'that's what she said' or a variation thereof I want it to always print michael?
python
asked 6 hours ago
John FrielJohn Friel
1061
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add a comment |
$begingroup$
I have a Jupyter notebook that allows a user to type in a phrase and the program will 'guess' which Office character it resembles.
Here is a link to the github repo
I have the following littered throughout my code:
the_office_raw_script['polarity'] = the_office_raw_script['line_text'].apply(lambda x: TextBlob(x).sentiment.polarity)
the_office_raw_script['scores'] = the_office_raw_script['line_text'].apply(lambda x: analyser.polarity_scores(x))
and the following, which is the prediction part:
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.naive_bayes import MultinomialNB
X_train, X_test, y_train, y_test = train_test_split(df_upsampled['line_text'], df_upsampled['speaker'], random_state = 0)
count_vect = CountVectorizer()
X_train_counts = count_vect.fit_transform(X_train)
tfidf_transformer = TfidfTransformer()
X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)
clf = MultinomialNB().fit(X_train_tfidf, y_train)
I have some examples of output, which is AWESOME! (so happy i got this far)
print(clf.predict(count_vect.transform(["hard working beet farmer"])))
the above prints dwight
print(clf.predict(count_vect.transform(["that's what she said"])))
the above prints pam which is totally wrong because michael says 'that's what she said' over 15 times throughout the series.
Two Questions
How would I go above making my script into a function, would that be worth it?
How do I somewhat ~hardcode~ some inputs? If someone types in 'that's what she said' or a variation thereof I want it to always print michael?
python
asked 6 hours ago
John FrielJohn Friel
1061
New contributor
John Friel 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 |
$begingroup$
I have a Jupyter notebook that allows a user to type in a phrase and the program will 'guess' which Office character it resembles.
Here is a link to the github repo
I have the following littered throughout my code:
the_office_raw_script['polarity'] = the_office_raw_script['line_text'].apply(lambda x: TextBlob(x).sentiment.polarity)
the_office_raw_script['scores'] = the_office_raw_script['line_text'].apply(lambda x: analyser.polarity_scores(x))
and the following, which is the prediction part:
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.naive_bayes import MultinomialNB
X_train, X_test, y_train, y_test = train_test_split(df_upsampled['line_text'], df_upsampled['speaker'], random_state = 0)
count_vect = CountVectorizer()
X_train_counts = count_vect.fit_transform(X_train)
tfidf_transformer = TfidfTransformer()
X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)
clf = MultinomialNB().fit(X_train_tfidf, y_train)
I have some examples of output, which is AWESOME! (so happy i got this far)
print(clf.predict(count_vect.transform(["hard working beet farmer"])))
the above prints dwight
print(clf.predict(count_vect.transform(["that's what she said"])))
the above prints pam which is totally wrong because michael says 'that's what she said' over 15 times throughout the series.
Two Questions
How would I go above making my script into a function, would that be worth it?
How do I somewhat ~hardcode~ some inputs? If someone types in 'that's what she said' or a variation thereof I want it to always print michael?
python
asked 6 hours ago
John FrielJohn Friel
1061
New contributor
John Friel is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I have a Jupyter notebook that allows a user to type in a phrase and the program will 'guess' which Office character it resembles.
Here is a link to the github repo
I have the following littered throughout my code:
the_office_raw_script['polarity'] = the_office_raw_script['line_text'].apply(lambda x: TextBlob(x).sentiment.polarity)
the_office_raw_script['scores'] = the_office_raw_script['line_text'].apply(lambda x: analyser.polarity_scores(x))
and the following, which is the prediction part:
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.naive_bayes import MultinomialNB
X_train, X_test, y_train, y_test = train_test_split(df_upsampled['line_text'], df_upsampled['speaker'], random_state = 0)
count_vect = CountVectorizer()
X_train_counts = count_vect.fit_transform(X_train)
tfidf_transformer = TfidfTransformer()
X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)
clf = MultinomialNB().fit(X_train_tfidf, y_train)
I have some examples of output, which is AWESOME! (so happy i got this far)
print(clf.predict(count_vect.transform(["hard working beet farmer"])))
the above prints dwight
print(clf.predict(count_vect.transform(["that's what she said"])))
the above prints pam which is totally wrong because michael says 'that's what she said' over 15 times throughout the series.
Two Questions
How would I go above making my script into a function, would that be worth it?
How do I somewhat ~hardcode~ some inputs? If someone types in 'that's what she said' or a variation thereof I want it to always print michael?
python
python
asked 6 hours ago
John FrielJohn Friel
1061
New contributor
John Friel is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 6 hours ago
John FrielJohn Friel
1061
New contributor
John Friel is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 6 hours ago
John FrielJohn Friel
1061
New contributor
John Friel is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 6 hours ago
John FrielJohn Friel
1061
asked 6 hours ago
John FrielJohn Friel
1061
1061
New contributor
John Friel is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
John Friel is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
John Friel 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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