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| # !pip install transformers | |
| from transformers import pipeline | |
| from client import AlpacaNewsFetcher | |
| from alpaca_trade_api import REST | |
| import os | |
| from dotenv import load_dotenv | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| from collections import defaultdict | |
| from datetime import date | |
| class NewsSentimentAnalysis: | |
| """ | |
| A class for sentiment analysis of news articles using the Transformers library. | |
| Attributes: | |
| - classifier (pipeline): Sentiment analysis pipeline from Transformers. | |
| """ | |
| def __init__(self): | |
| """ | |
| Initializes the NewsSentimentAnalysis object. | |
| """ | |
| self.classifier = pipeline('sentiment-analysis') | |
| def analyze_sentiment(self, news_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. | |
| """ | |
| summary = news_article['summary'] | |
| title = news_article['title'] | |
| timestamp = news_article['timestamp'] | |
| relevant_text = summary + title | |
| sentiment_result = self.classifier(relevant_text) | |
| analysis_result = { | |
| 'timestamp': timestamp, | |
| 'title': title, | |
| 'summary': summary, | |
| 'sentiment': sentiment_result | |
| } | |
| return analysis_result | |
| def plot_sentiment_graph(self, sentiment_analysis_result): | |
| """ | |
| Plots a sentiment analysis graph | |
| 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_result) | |
| df['Timestamp'] = pd.to_datetime(df['Timestamp']) | |
| df['Date'] = df['Timestamp'].dt.date | |
| #Group by Date, sentiment value count | |
| grouped = df.groupby(by='Date')['Sentiment'].value_counts() | |
| grouped.plot.pie() | |
| def get_dominant_sentiment (self, sentiment_analysis_result): | |
| """ | |
| 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_result) | |
| df['Timestamp'] = pd.to_datetime(df['Timestamp']) | |
| df['Date'] = df['Timestamp'].dt.date | |
| #Group by Date, sentiment value count | |
| grouped = df.groupby(by='Date')['Sentiment'].value_counts() | |
| df = pd.DataFrame(list(grouped.items()), columns=['Sentiment', 'count']) | |
| df['date'] = df['Sentiment'].apply(lambda x: x[0]) | |
| df['sentiment'] = df['Sentiment'].apply(lambda x: x[1]) | |
| df.drop('Sentiment', axis=1, inplace=True) | |
| result = df.groupby('sentiment')['count'].sum().reset_index() | |
| # Determine the sentiment with the most count | |
| dominant_sentiment = result.loc[result['count'].idxmax()] | |
| return dominant_sentiment | |
| #starting point of the program | |
| if __name__ == '__main__': | |
| # Example Usage: | |
| # Initialize the AlpacaNewsFetcher object | |
| #Load Alpaca Key and Secret from environment. | |
| load_dotenv() | |
| api_key = os.environ["ALPACA_API_KEY"] | |
| api_secret = os.environ["ALPACA_SECRET"] | |
| #Initialize AlpacaNewsFetcher, a class for fetching news articles related to a specific stock from Alpaca API. | |
| news_fetcher = AlpacaNewsFetcher(api_key, api_secret) | |
| # Fetch news (contains - title of the news, timestamp and summary) for AAPL from 2021-01-01 to 2021-12-31 | |
| news_data = news_fetcher.fetch_news("AAPL", "2021-01-01", "2021-12-31") | |
| # Initialize the NewsSentimentAnalysis object | |
| news_sentiment_analyzer = NewsSentimentAnalysis() | |
| analysis_result = [] | |
| # Assume 'news_data' is a list of news articles (each as a dictionary) | |
| for article in news_data: | |
| sentiment_analysis_result = news_sentiment_analyzer.analyze_sentiment(article) | |
| # Display sentiment analysis results | |
| """ print(f'Timestamp: {sentiment_analysis_result["timestamp"]}, ' | |
| f'Title: {sentiment_analysis_result["title"]}, ' | |
| f'Summary: {sentiment_analysis_result["summary"]}') | |
| print(f'Sentiment: {sentiment_analysis_result["sentiment"]}', '\n') """ | |
| #Extracting timestamp of article and sentiment of article for graphing | |
| result = { | |
| 'Timestamp': sentiment_analysis_result["timestamp"], | |
| 'News- Title:Summary': sentiment_analysis_result["title"] + sentiment_analysis_result["summary"], | |
| 'Sentiment': sentiment_analysis_result["sentiment"][0]['label'] | |
| } | |
| analysis_result.append(result) | |
| #Graph dominant sentiment based on sentiment analysis data of news articles | |
| dominant_sentiment = news_sentiment_analyzer.get_dominant_sentiment(analysis_result) | |
| final_result = { | |
| 'Sentiment-analysis-result' : analysis_result, | |
| 'Dominant-sentiment' : dominant_sentiment['sentiment'] | |
| } | |
| print(final_result) | |