In this tutorial, we will be incorporating Scikit-Learn with Natural Language Processing (NLP) using Python and NLTK (Natural Language Toolkit). Scikit-Learn is a powerful machine learning library that enables us to build predictive models using various algorithms. NLTK, on the other hand, is a popular NLP library in Python that provides tools for processing and analyzing natural language text.
To get started, make sure you have both Scikit-Learn and NLTK installed in your Python environment. You can install these libraries using pip:
pip install scikit-learn
pip install nltkOnce you have the necessary libraries installed, you can import them in your Python script:
import nltk
nltk.download('punkt')
from nltk.tokenize import word_tokenize
from sklearn.feature_extraction.text import CountVectorizerIn this tutorial, we will be working with a sample dataset of movie reviews. Our goal is to build a predictive model that can classify movie reviews as positive or negative based on the text content.
First, let’s load our dataset and preprocess the text data. We will tokenize the text using NLTK’s word_tokenize function and then convert the text into a bag of words representation using Scikit-Learn’s CountVectorizer:
reviews = ['This movie is great', 'I hated this movie', 'The plot was predictable']
labels = [1, 0, 0] # 1 for positive, 0 for negative
# Tokenize text data
tokenized_reviews = [word_tokenize(review.lower()) for review in reviews]
# Convert text data into a bag of words representation
vectorizer = CountVectorizer()
X = vectorizer.fit_transform([' '.join(review) for review in tokenized_reviews])Now that we have preprocessed our text data, we can split it into training and testing sets, and train a predictive model using Scikit-Learn’s machine learning algorithms. In this tutorial, we will use a simple logistic regression model:
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
X_train, X_test, y_train, y_test = train_test_split(X, labels, test_size=0.2, random_state=42)
model = LogisticRegression()
model.fit(X_train, y_train)
# Evaluate the model
accuracy = model.score(X_test, y_test)
print(f'Accuracy: {accuracy}')By following this tutorial, you have learned how to incorporate Scikit-Learn with Natural Language Processing using Python and NLTK. You can further enhance your model by experimenting with different algorithms, hyperparameters, and preprocessing techniques. NLP and machine learning are powerful tools that can be applied to a wide range of text classification tasks, so feel free to explore and experiment with your own datasets and projects.
The voting is bagging ensemble right?
Hey It's the end of 2020 and I started this course and it's really helpful but I got a problem which is related to SGDClassifier which happens when the program reaches out to the line where the accuracy of SGDClassifier is going to be calculated:
Traceback (most recent call last):
File "c:UsersaliseDesktoptwitter-data-scrapernltk-sentdextest.py", line 86, in <module>
(nltk.classify.accuracy(SGDClassifier_Classifier, testing_set))*100)
File "C:UsersaliseAppDataRoamingPythonPython37site-packagesnltkclassifyutil.py", line 91, in accuracy
results = classifier.classify_many([fs for (fs, l) in gold])
File "C:UsersaliseAppDataRoamingPythonPython37site-packagesnltkclassifyscikitlearn.py", line 80, in classify_many
X = self._vectorizer.transform(featuresets)
File "C:Program Files (x86)Python37-32libsite-packagessklearnfeature_extraction_dict_vectorizer.py", line 289, in transform
return self._transform(X, fitting=False)
File "C:Program Files (x86)Python37-32libsite-packagessklearnfeature_extraction_dict_vectorizer.py", line 150, in _transform
feature_names = self.feature_names_
AttributeError: 'DictVectorizer' object has no attribute 'feature_names_'
Been searching for 1 hour but didn't find anything because I don't anything about sci-kit learn 🙂
Why is the accuracy changing even though the code is the same ?
it is clear that you have no experience in NLTK, BUT your course is great for beginners
I want to thank you, I was really lost in sentiment analysis and the use of classifiers. I got the base to acheive my "text mining" course.
I have warnings like this
C:UsersLENOVOPycharmProjectssentdexTutorialsNLPvenvlibsite-packagessklearnlinear_modellogistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.
FutureWarning)
LogisticRegression accuracy percent: 83.0
C:UsersLENOVOPycharmProjectssentdexTutorialsNLPvenvlibsite-packagessklearnlinear_modelstochastic_gradient.py:166: FutureWarning: max_iter and tol parameters have been added in SGDClassifier in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.
FutureWarning)
I think it has something to do with the parameters
so the SklearnClassifier() always needs of a naïve_bayes algorithm? as a parameter?
Very good tutorial! I've been learning nltk but now I meet some problems when incorporating with sklearn. it seems sklearn provides completely different w2v method from nltk. error occurs when I use nltk to include bigram for trainning… also, does sklearn provides any methods like show_most_informative_features? thanks!
Hi Dex! How can I write my own text, and see how it is classified by my classifier?
I tried nltk.classify.accuracy(classifier, text) where the classifier was the original naive bayes, and the text is a string like "Best movie ever!"… it always returns "neg" though.
Question: how can we find the CONFUSION MATRIX and also ROC of these classifiers.
how to Find all locations / cities / places in a text ??
Do results vary from OS to OS… because I am using MAC and getting results like:
Original Naive bayes algo Accuracy: 88.0
MNB classifier algo Accuracy: 84.0
BNB classifier algo Accuracy: 83.0
Linear Reg classifier algo Accuracy: 77.0
SGD classifier algo Accuracy: 81.0
SVC classifier algo Accuracy: 82.0
LSVC classifier algo Accuracy: 75.0
NUSVC classifier algo Accuracy: 81.0
Hi Harrison,
I used the positive and negative txt files which were provided and got a good accuracy % of ~80 % but when i chose a different data set both negative and positive texts being 4 mb each my accuracy dropped to ~ 60% . Can you please please help me on this or recommend another approach ?
Rewards to anyone who can actually make a for loop for that! O.O
MNB_classifier accuracy percent: 82.0
BernoulliNB_classifier accuracy percent: 80.0
LogisticRegression_classifier accuracy percent: 81.0
D:SoftAnaconda3libsite-packagessklearnlinear_modelstochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.
"and default tol will be 1e-3." % type(self), FutureWarning)
SGDClassifier_classifier accuracy percent: 81.0
SVC_classifier accuracy percent: 80.0
LinearSVC_classifier accuracy percent: 81.0
NuSVC_classifier accuracy percent: 86.0
sir, it shows extra this lines. I don't know why. can you tell me please. thanks
sir ! u teach so well and u are smart as well! feels good when u chuckle 😀
all my classifiers are giving same accuracy every time why is this happening ?
why are u loading from naivebays.pickle? You are not saving anything there. As I remember u have commented it out.
Has anyone been able to solve the code for the GaussianNB?
I am facing following error while executing code. : –
File "/usr/local/lib/python2.7/dist-packages/nltk/classify/scikitlearn.py", line 69, in _init_
self._encoder = LabelEncoder()
NameError: global name 'LabelEncoder' is not defined
I tried to find some solutions on google. Most of suggestions are related to upgrading Numpy or Sci-kit learn. I have tried this ,but it's not working.