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) for submission in reddit.subreddit('washingtondc').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('washingtondc').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('washingtondc').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 with their sentiment, hyperlink and date in front of the article text, the articles MUST be ordered by date!, and generate 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. Make sure the articles as well as the links are ordered descending by Date!!!!!!! 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)