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| import nltk | |
| nltk.download('punkt') # Download the required resource | |
| import requests | |
| from bs4 import BeautifulSoup | |
| import time | |
| from nltk.sentiment import SentimentIntensityAnalyzer | |
| from nltk.tokenize import word_tokenize | |
| from nltk.corpus import stopwords | |
| from collections import Counter | |
| from gtts import gTTS | |
| import os | |
| import platform | |
| # Download required NLTK data files. | |
| nltk.download('vader_lexicon') | |
| nltk.download('averaged_perceptron_tagger') | |
| nltk.download('stopwords') | |
| def get_bing_news_articles(company_name, num_articles=10): | |
| """ | |
| Scrapes Bing News search results for a given company name. | |
| """ | |
| query = company_name.replace(" ", "+") | |
| url = f"https://www.bing.com/news/search?q={query}&FORM=HDRSC6" | |
| headers = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64)"} | |
| response = requests.get(url, headers=headers) | |
| if response.status_code != 200: | |
| return [] | |
| soup = BeautifulSoup(response.text, "html.parser") | |
| articles = [] | |
| news_cards = soup.find_all("div", class_="news-card") | |
| for card in news_cards: | |
| title_tag = card.find("a", class_="title") | |
| if not title_tag: | |
| continue | |
| title = title_tag.get_text(strip=True) | |
| article_url = title_tag.get("href") | |
| snippet_tag = card.find("div", class_="snippet") | |
| snippet = snippet_tag.get_text(strip=True) if snippet_tag else "" | |
| source_tag = card.find("div", class_="source") | |
| source = source_tag.get_text(strip=True) if source_tag else "" | |
| articles.append({ | |
| "title": title, | |
| "summary": snippet, | |
| "url": article_url, | |
| "source": source | |
| }) | |
| if len(articles) >= num_articles: | |
| break | |
| return articles | |
| def analyze_sentiment(text): | |
| """ | |
| Analyzes the sentiment of the given text using NLTK's VADER. | |
| Returns: | |
| sentiment (str): "Positive", "Negative", or "Neutral" | |
| scores (dict): The full set of polarity scores. | |
| """ | |
| sia = SentimentIntensityAnalyzer() | |
| scores = sia.polarity_scores(text) | |
| compound = scores["compound"] | |
| if compound >= 0.05: | |
| sentiment = "Positive" | |
| elif compound <= -0.05: | |
| sentiment = "Negative" | |
| else: | |
| sentiment = "Neutral" | |
| return sentiment, scores | |
| def extract_topics(text): | |
| """ | |
| Extracts topics from the input text using basic noun extraction. | |
| Tokenizes the text, removes stopwords and punctuation, and returns a list of unique nouns. | |
| """ | |
| text = text.lower() | |
| tokens = word_tokenize(text) | |
| stop_words = set(stopwords.words("english")) | |
| filtered = [word for word in tokens if word.isalpha() and word not in stop_words] | |
| tagged = nltk.pos_tag(filtered) | |
| nouns = [word for word, pos in tagged if pos in ["NN", "NNS", "NNP", "NNPS"]] | |
| return list(set(nouns)) | |
| def comparative_analysis(articles): | |
| """ | |
| Performs comparative analysis across articles. | |
| """ | |
| sentiment_distribution = {"Positive": 0, "Negative": 0, "Neutral": 0} | |
| sales_keywords = {"sales", "growth", "record", "profit"} | |
| regulatory_keywords = {"regulation", "regulatory", "scrutiny", "lawsuit", "legal", "compliance"} | |
| sales_count = 0 | |
| reg_count = 0 | |
| all_topics = [] | |
| for article in articles: | |
| sentiment = article.get("sentiment", "Neutral") | |
| sentiment_distribution[sentiment] += 1 | |
| combined_text = f"{article['title']} {article['summary']}".lower() | |
| if any(keyword in combined_text for keyword in sales_keywords): | |
| sales_count += 1 | |
| if any(keyword in combined_text for keyword in regulatory_keywords): | |
| reg_count += 1 | |
| topics = extract_topics(combined_text) | |
| article["topics"] = topics | |
| all_topics.extend(topics) | |
| if sales_count > reg_count: | |
| coverage_insight = (f"More articles ({sales_count}) emphasize sales and financial growth compared to regulatory concerns ({reg_count}).") | |
| elif reg_count > sales_count: | |
| coverage_insight = (f"More articles ({reg_count}) focus on regulatory or legal challenges compared to sales aspects ({sales_count}).") | |
| else: | |
| coverage_insight = (f"An equal number of articles emphasize sales/growth and regulatory issues ({sales_count} each).") | |
| topic_counter = Counter(all_topics) | |
| common_topics = [topic for topic, count in topic_counter.items() if count > 1] | |
| unique_topics = {} | |
| for i, article in enumerate(articles, start=1): | |
| unique = [topic for topic in article.get("topics", []) if topic_counter[topic] == 1] | |
| unique_topics[f"Article {i}"] = unique | |
| analysis = { | |
| "Sentiment Distribution": sentiment_distribution, | |
| "Coverage Differences": coverage_insight, | |
| "Topic Overlap": { | |
| "Common Topics": common_topics, | |
| "Unique Topics": unique_topics | |
| } | |
| } | |
| return analysis | |
| def convert_text_to_hindi_tts(text, output_file="output.mp3"): | |
| """ | |
| Converts the input text into Hindi speech using gTTS and saves it as an MP3 file. | |
| """ | |
| tts = gTTS(text=text, lang='hi', slow=False) | |
| tts.save(output_file) | |
| return output_file | |
| def play_audio(file_path): | |
| """ | |
| Plays an audio file using the system's default media player. | |
| """ | |
| if platform.system() == "Windows": | |
| os.startfile(file_path) | |
| elif platform.system() == "Darwin": | |
| os.system(f"open {file_path}") | |
| else: | |
| os.system(f"mpg123 {file_path}") | |