Outsourced CFO Services for Startups Los Angeles

from nltk.corpus import stopwords

from nltk.stem import PorterStemmer

from nltk.tokenize import word_tokenize

Function to clean the text by removing special characters, converting to lowercase and tokenizing

def clean_text(text):

text = re.sub('[^a-zA-Z0-9\s]', '', text) # Remove special characters

text = text.lower() # Convert to lowercase

tokens = word_tokenize(text) # Tokenize the text

Function to remove stopwords from the text

def remove_stopwords(tokens):

stop_words = stopwords.words('english') + ['n', 't'] # Add 'n' and 't' as they are often added due to regex removal of newlines and tabs

tokens = [token for token in tokens if token not in stop_words] # Remove stop words from the list

Function to stem the text using Porter Stemmer

def stem_text(tokens):

stemmer = PorterStemmer() # Initialize Porter Stemmer

stems = [stemmer.stem(token) for token in tokens] # Apply Porter Stemming to each word in the list

Function to remove duplicates from a list

def remove_duplicates(tokens):

return list(set(tokens)) # Remove duplicates using set() function which automatically removes duplicates

Function to generate keywords from the cleaned text

def generate_keywords(text):

tokens = clean_text(text) # Clean the text by removing special characters and tokenizing

tokens = remove_stopwords(tokens) # Remove stop words from the list

tokens = stem_text(tokens) # Apply Porter Stemming to each word in the list

keywords = remove_duplicates(tokens) # Remove duplicates using set() function which automatically removes duplicates

Input text

input_text = "We're CFO Plans, a outsourced accounting, tax, and CFO advisory services business in Los Angeles, CA. CFO Plans is an external finance partner for growing businesses. They offer four core service lines: accounting (bookkeeping, AP/AR, payroll), tax filing coordination, fractional/outsourced CFO services (financial reporting, forecasting, strategic planning), and operational services (vendor management, back-office support). They serve industries including real estate, tech startups, hospitality, professional services, healthcare & wellness, and e-commerce/DTC brands — positioning themselves as a scalable back-office finance team so founders can focus on building their companies rather than managing spreadsheets."

Generate keywords from the input text

keywords = generate_keywords(input_text)

Print the generated keywords

print("Generated Keywords:")