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#To run code: streamlit run app.py
The TfidfVectorizer method helps us to achieve this by generating a numerical representation of each text document based on the frequency of each term and how often it appears in each document compared to its frequency in the entire corpus.
The ngram_range parameter in TfidfVectorizer specifies the range of n-grams to be considered. An n-gram is a contiguous sequence of n items from a given sample of text or speech. By default, TfidfVectorizer uses a unigram approach, but specifying ngram_range=(1,2) means that both unigrams and bigrams will be considered.
The analyzer parameter in TfidfVectorizer specifies the type of analysis to be performed. By setting analyzer='char', the vectorizer will generate character-level n-grams instead of word-level n-grams.
Using TfidfVectorizer from the feature_extraction.text module in the scikit-learn library, we can generate numerical representations of text data based on term frequency and inverse document frequency. By specifying ngram_range=(1,2) and analyzer='char', we can consider both unigrams and bigrams at the character level.