How can I determine support and confident keywords in data mining?

 In the context of data science the data mining of the keywords is one of the important components. To determine the keyword that supports and confidence in data mining, you can deploy techniques such as TF-IDF ( Team Frequency- Inverse Document Frequency)

Firstly, you should calculate TF-IDF scores for words in all documents. The higher TF-IDF values indicate stronger relevancy of the word.

Secondly, use algorithms such as Apriori or FP-growth to find the phrases that are co-occurring.

Here is the example given for using the Natural Language Toolkit (NLTK) of Python for TF-IDF analysis.

From nltk.corpus import stopwords
From nltk.tokenize import word_tokenize
From nltk.stem import WordNetLemmatizer
From sklearn.feature_extraction.text import TfidfVectorizer
# Sample documents
Documents = [
    “This is the first document.”,
    “This document is the second document.”,
    “And this is the third one.”,
    “Is this the first document?”
]
# Initialize TF-IDF Vectorizer
Tfidf_vectorizer = TfidfVectorizer()
# Tokenization and preprocessing
Lemmatizer = WordNetLemmatizer()
Preprocessed_documents = []
For doc in documents:
    # Tokenize words
    Words = word_tokenize(doc. lower())
    # Remove stopwords and non-alphabetic tokens, lemmatize words
    Filtered_words = [lemmatizer. lemmatize(word) for a word in words if word. Is alpha () and word not in stopwords.words(Englishh)]
    Preprocessed_documents.append(‘ ‘.join(filtered_words))
# Fit and transform documents to TF-IDF vectors
Tfidf_vectors = tfidf_vectorizer.fit_transform(preprocessed_documents)
# Display the TF-IDF matrix
Print(tfidf_vectors.to array())