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renamed extract_headline to extract_article, reduced the function cal… #18

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95 changes: 47 additions & 48 deletions modules/headline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,52 +4,51 @@


class Title:
# Initialisations
def __init__(self):
self.news_url = input("\nEnter The URL : ")
self.pos = [] # Variable to store all positive tokens from positive_headlines.csv file
self.neg = [] # Variable to store all negative tokens from negative_headlines.csv file
# self.article = newspaper.Article(news_url)

# extract headline
def extract_headline(self):
try:
article = newspaper.Article(self.news_url)
article.download()
article.parse()
return article
except newspaper.article.ArticleException: # List possible errors in case of any exception
print("\nCONNECTION/URL ERROR: Article could not be retrieved.")

# Adding Training/Testing Data
def train(self, headline):
with open("positive_headlines.csv") as file:
for sentence in file:
self.pos.append([{word: True for word in nltk.word_tokenize(sentence)}, 'Positive'])

with open("negative_headlines.csv") as file:
for sentence in file:
self.neg.append([{word: True for word in nltk.word_tokenize(sentence)}, 'Negative'])

training = self.pos[:int(len(self.pos))] + self.neg[:int(len(self.neg))]

classifier = NaiveBayesClassifier.train(training) # Training
sentiment = classifier.classify({word: True for word in nltk.word_tokenize(headline)})
return sentiment

# categorize headline
def headline_category(self, headline, sentiment):
print("\nHEADLINE :", headline.upper())
print("SENTIMENT :", sentiment)
print("AUTHOR(S) :", self.extract_headline().authors, '\n')

# main of class
def main(self):
hdln = self.extract_headline().title.strip()
sntmnt = self.train(hdln)
self.train(hdln)
self.headline_category(hdln, sntmnt)


# Initialisations
def __init__(self):
self.news_url = input("\nEnter The URL : ")
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We should consider working with config files, requirement files and readme.md's from now on. This improves readability, efficiency and minimizes the chance someone fricks with the code.

self.pos = [] # Variable to store all positive tokens from positive_headlines.csv file
self.neg = [] # Variable to store all negative tokens from negative_headlines.csv file
self.article = newspaper.Article(self.news_url)

# extract headline
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# extract article

def extract_article(self):
try:
self.article.download()
self.article.parse()
return self.article
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There is no need to return the article since only the connection adapters need to be downloaded and parsed. Returning does not do anything since the article is already defined in self.article.

except newspaper.article.ArticleException: # List possible errors in case of any exception
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For this issue; #17
We could also simply say;

except newspaper.article.ArticleException as e:
    print(e)

print("\nCONNECTION/URL ERROR: Article could not be retrieved.")

# Adding Training/Testing Data
def train(self, headline):
with open("positive_headlines.csv") as file:
for sentence in file:
self.pos.append([{word: True for word in nltk.word_tokenize(sentence)}, 'Positive'])

with open("negative_headlines.csv") as file:
for sentence in file:
self.neg.append([{word: True for word in nltk.word_tokenize(sentence)}, 'Negative'])

training = self.pos[:int(len(self.pos))] + self.neg[:int(len(self.neg))]

classifier = NaiveBayesClassifier.train(training) # Training
sentiment = classifier.classify({word: True for word in nltk.word_tokenize(headline)})
return sentiment

def headline_category(self, headline, sentiment):
print("\nHEADLINE :", headline.upper())
print("SENTIMENT :", sentiment)
print("AUTHOR(S) :", self.article.authors, '\n')

# main of class
def main(self):
article = self.extract_article()
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Why would you define article here if self.article is already initiated?
There are 2 ways around this;

  1. Instead of article = self.extract_article() say self.extract_article()
  2. In def __init__ make self.article = self.extract_article() where the entired article is initiated, downloaded and parsed. Here you would want the return.

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I prefer 2

headline = article.title.strip()
sntmnt = self.train(headline)
self.train(headline)
self.headline_category(headline, sntmnt)


if __name__ == '__main__':
Title().main()
Title().main()