Spaces:
Sleeping
Sleeping
File size: 5,928 Bytes
38b6b6d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 |
import requests
import json
import os
from dotenv import load_dotenv
from transformers import pipeline
import os
import pandas as pd
from collections import defaultdict
from datetime import date
import matplotlib.pyplot as plt
import http.client, urllib.parse
from GoogleNews import GoogleNews
from langchain_openai import ChatOpenAI
def fetch_news(stockticker):
""" Fetches news articles for a given stock symbol within a specified date range.
Args:
- stockticker (str): Symbol of a particular stock
Returns:
- list: A list of dictionaries containing stock 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(stockticker)
news_json=googlenews.get_texts()
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
}
news_article_list.append(relevant_info)
counter+=1
return news_article_list
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='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 "},
{"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=(8, 8))
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 fig
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
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 stock 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)
|