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import os
from dotenv import load_dotenv
from transformers import pipeline
import pandas as pd
from GoogleNews import GoogleNews
from langchain_openai import ChatOpenAI
import praw
from datetime import datetime
load_dotenv()
def fetch_news(topic):
""" Fetches news articles within a specified date range.
Args:
- topic (str): Topic of interest
Returns:
- list: A list of dictionaries containing news. """
load_dotenv()
days_to_fetch_news = os.environ["DAYS_TO_FETCH_NEWS"]
googlenews = GoogleNews()
googlenews.set_period(days_to_fetch_news)
googlenews.get_news(topic)
news_json=googlenews.get_texts()
urls=googlenews.get_links()
no_of_news_articles_to_fetch = os.environ["NO_OF_NEWS_ARTICLES_TO_FETCH"]
news_article_list = []
counter = 0
for article in news_json:
if(counter >= int(no_of_news_articles_to_fetch)):
break
relevant_info = {
'News_Article': article,
'URL': urls[counter]
}
news_article_list.append(relevant_info)
counter+=1
return news_article_list
def fetch_reddit_news(topic):
load_dotenv()
REDDIT_USER_AGENT= os.environ["REDDIT_USER_AGENT"]
REDDIT_CLIENT_ID= os.environ["REDDIT_CLIENT_ID"]
REDDIT_CLIENT_SECRET= os.environ["REDDIT_CLIENT_SECRET"]
#https://medium.com/geekculture/a-complete-guide-to-web-scraping-reddit-with-python-16e292317a52
user_agent = REDDIT_USER_AGENT
reddit = praw.Reddit (
client_id= REDDIT_CLIENT_ID,
client_secret= REDDIT_CLIENT_SECRET,
user_agent=user_agent
)
headlines = set ( )
for submission in reddit.subreddit('nova').search(topic,time_filter='week'):
headlines.add(submission.title + ', Date: ' +datetime.utcfromtimestamp(int(submission.created_utc)).strftime('%Y-%m-%d %H:%M:%S') + ', URL:' +submission.url)
if len(headlines)<10:
for submission in reddit.subreddit('nova').search(topic,time_filter='year'):
headlines.add(submission.title + ', Date: ' +datetime.utcfromtimestamp(int(submission.created_utc)).strftime('%Y-%m-%d %H:%M:%S') + ', URL:' +submission.url)
if len(headlines)<10:
for submission in reddit.subreddit('nova').search(topic): #,time_filter='week'):
headlines.add(submission.title + ', Date: ' +datetime.utcfromtimestamp(int(submission.created_utc)).strftime('%Y-%m-%d %H:%M:%S') + ', URL:' +submission.url)
return headlines
def analyze_sentiment(article):
"""
Analyzes the sentiment of a given news article.
Args:
- news_article (dict): Dictionary containing 'summary', 'headline', and 'created_at' keys.
Returns:
- dict: A dictionary containing sentiment analysis results.
"""
#Analyze sentiment using default model
#classifier = pipeline('sentiment-analysis')
#Analyze sentiment using specific model
classifier = pipeline(model='tabularisai/robust-sentiment-analysis') #mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis')
sentiment_result = classifier(str(article))
analysis_result = {
'News_Article': article,
'Sentiment': sentiment_result
}
return analysis_result
def generate_summary_of_sentiment(sentiment_analysis_results): #, dominant_sentiment):
news_article_sentiment = str(sentiment_analysis_results)
print("News article sentiment : " + news_article_sentiment)
os.environ["OPENAI_API_KEY"] = os.environ["OPENAI_API_KEY"]
model = ChatOpenAI(
model="gpt-4o",
temperature=0,
max_tokens=None,
timeout=None,
max_retries=2,
# api_key="...", # if you prefer to pass api key in directly instaed of using env vars
# base_url="...",
# organization="...",
# other params...
)
messages=[
{"role": "system", "content": "You are a helpful assistant that looks at all news articles, their sentiment, along with domainant sentiment and generates a summary rationalizing dominant sentiment. At the end of the summary, add URL links with dates for all the articles in the markdown format for streamlit. Example of adding the URLs: The Check out the links: [link](%s) % url, 2024-03-01 "},
{"role": "user", "content": f"News articles and their sentiments: {news_article_sentiment}"} #, and dominant sentiment is: {dominant_sentiment}"}
]
response = model.invoke(messages)
summary = response.content
print ("+++++++++++++++++++++++++++++++++++++++++++++++")
print(summary)
print ("+++++++++++++++++++++++++++++++++++++++++++++++")
return summary
def plot_sentiment_graph(sentiment_analysis_results):
"""
Plots a sentiment analysis graph
Args:
- sentiment_analysis_result): (dict): Dictionary containing 'Review Title : Summary', 'Rating', and 'Sentiment' keys.
Returns:
- dict: A dictionary containing sentiment analysis results.
"""
df = pd.DataFrame(sentiment_analysis_results)
print(df)
#Group by Rating, sentiment value count
grouped = df['Sentiment'].value_counts()
sentiment_counts = df['Sentiment'].value_counts()
# Plotting pie chart
# fig = plt.figure(figsize=(5, 3))
# plt.pie(sentiment_counts, labels=sentiment_counts.index, autopct='%1.1f%%', startangle=140)
# plt.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
#Open below when u running this program locally and c
#plt.show()
return sentiment_counts
def get_dominant_sentiment (sentiment_analysis_results):
"""
Returns overall sentiment, negative or positive or neutral depending on the count of negative sentiment vs positive sentiment
Args:
- sentiment_analysis_result): (dict): Dictionary containing 'summary', 'headline', and 'created_at' keys.
Returns:
- dict: A dictionary containing sentiment analysis results.
"""
df = pd.DataFrame(sentiment_analysis_results)
# Group by the 'sentiment' column and count the occurrences of each sentiment value
print(df)
print(df['Sentiment'])
sentiment_counts = df['Sentiment'].value_counts().reset_index()
sentiment_counts.columns = ['sentiment', 'count']
print(sentiment_counts)
# Find the sentiment with the highest count
dominant_sentiment = sentiment_counts.loc[sentiment_counts['count'].idxmax()]
return dominant_sentiment['sentiment']
#starting point of the program
if __name__ == '__main__':
#fetch news
news_articles = fetch_news('AAPL')
analysis_results = []
#Perform sentiment analysis for each product review
for article in news_articles:
sentiment_analysis_result = analyze_sentiment(article['News_Article'])
# Display sentiment analysis results
print(f'News Article: {sentiment_analysis_result["News_Article"]} : Sentiment: {sentiment_analysis_result["Sentiment"]}', '\n')
result = {
'News_Article': sentiment_analysis_result["News_Article"],
'Sentiment': sentiment_analysis_result["Sentiment"][0]['label']
}
analysis_results.append(result)
#Graph dominant sentiment based on sentiment analysis data of reviews
dominant_sentiment = get_dominant_sentiment(analysis_results)
print(dominant_sentiment)
#Plot graph
plot_sentiment_graph(analysis_results)
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