Google_News_Extraction_Article

get_org_counts.py

# Create a function to get a count of the top n organizations mentioned in the article with counts
def get_org_counts(text):
    # Remove linebreaks from the text
    text = text.replace("\n"," paragraph break ")
    doc = nlp(text)
    
    # Loop through the doc object and extract ORG (organization) entities
    res = []
    for ent in doc.ents:
        if ent.label_ == 'ORG':
            res.append(ent.text.lower().replace("'s", "").replace(", inc.", "")) # some text standardization
    
    # The word company is extracted a lot but is useless, so remove this.
    res = [i for i in res if i != 'company']

    # Create a dictionary that counts the number of times a word is mentioned
# https://stackoverflow.com/questions/61712565/count-words-in-a-list-and-add-them-to-a-dictionary-along-with-number-of-occurre
    word_count = {}
    for item in res:
        if item in word_count:
            word_count[item] += 1

        else:
            word_count[item] = 1
    
    # Return a sorted dictionary of the org counts
    n=3
    top_n_org = dict(sorted(word_count.items(), key= lambda x: x[1], reverse=True)[:n])
    
    return(top_n_org)