Natural Language Processing (NLP)

Create a bag-of-words model using CountVectorizer.

Capitalize the first letter of each word in a sentence.

Check if a string contains any digits.

Check if a word is a palindrome (reads same forwards and backwards).

Check if one string contains another string.

Convert all tokens to lowercase.

Count the number of characters in a string (including spaces).

Count how many times a specific word appears in text.

Count the number of vowels in a string.

Count how many times each word appears in a text.

Count the number of words in a string.

Extract all numbers from a string.

Find the index of the first occurrence of a word.

Find the longest word in a sentence.

Join a list of words into a single string with spaces.

Detect language of text using langdetect.

Reduce words to their dictionary form using WordNetLemmatizer.

Identify named entities in text using NLTK.

Tag words with their part-of-speech using NLTK.

Remove all digits from a string.

Remove punctuation from a list of tokens.

Remove common stopwords from text using NLTK.

Remove extra whitespace from the beginning and end of a string.

Repeat a string multiple times.

Replace all occurrences of a word in a sentence.

Reverse the characters in a string.

Analyze sentiment polarity of a sentence.

Return a predefined response based on input text.

Create a rule-based response system.

Correct spelling in a sentence using TextBlob.

Split a string into a list of characters.

Reduce words to their stems using Porter Stemmer.

Swap uppercase and lowercase letters in a string.

Generate text using a simple Markov chain.

Calculate cosine similarity between two sentences.

Extract key sentences from text using NLTK.

Convert text to TF-IDF vectors.

Split a sentence into words using NLTK's word_tokenize.

Create a word cloud from text.

Load and use pre-trained GloVe word vectors.