Step 4- Vectorize the text data using TF-IDF We will vectorize the data to convert it into numerical form, which would be the training input. vectorizer = TfidfVectorizer(stop_words='english') X_train = vectorizer.fit_transform(df_train['text']) X_test = vectorizer.transform(df_test['...
To understand this code, please refer to the below table:- TfidVectorizer The TfidfVectorizer turns a set of raw documents into a TF-IDF feature matrix. Python implementation of Us with and Word2Vec word embeddings. fit_transform It is used to train data in order to scale it and learn ...
But before training the model, we need to transform our cleaned reviews into numerical values so that the model can understand the data. In this case, we will use the TfidfVectorizer method from scikit-learn. TfidfVectorizer will help us to convert a collection of text documents to a matr...
Now, we will create aTF-IDFvector of the tweet column using theTfidfVectorizerand we will pass the parameter lowercase as True so that it will first convert text to lowercase. We will also keep max features as 1000 and pass the predefined list of stop words present in the scikit-learn l...
importrequestsfrombs4importBeautifulSoupimportpandasaspdfromdatetimeimportdatetimefromtqdmimporttqdm,tqdm_notebookdefgetSources():source_url='https://newsapi.org/v1/sources?language=en'response=requests.get(source_url).json()sources=[]forsourceinresponse['sources']:sources.append(source['id'])returnsou...
To compute the cosine similarity, you need the word count of the words in each document.The CountVectorizer or the TfidfVectorizer from scikit learn lets us compute this.The output of this comes as a sparse_matrix.On this, am optionally converting it to a pandas dataframe to see the word ...
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