My code is :
import pandas as pd
data = pd.read_table('train.tsv')
X=data.Phrase
Y=data.Sentiment
from sklearn import cross_validation
X_train,X_test,Y_train,Y_test=cross_validation.train_test_split(X,Y,test_size=0.2,random_state=0)
from sklearn.naive_bayes import MultinomialNB
clf = MultinomialNB()
clf.fit(X,Y)
I get the error :ValueError: could not convert string to float:
What changes can I make that my code works?
You can't pass in text data into MultinomialNB of scikit-learn as stated in its documentation.
None of the algorithms in scikit-learn works directly with text data. You need to do some preprocessing to get desired output. You'll need to first extract the features from text data using techniques like bagging or tokenizing. Have a look at this link for better understanding.
You also might want to look at using NLTK for such use cases as yours.
ValueError when using Multinomial Naive Bayes classifier
You probably should preprocess your data as shown in the answer above.
