Sentiment Analysis for Predicting Market Trends with Generative AI

Understanding market sentiment is a critical part of developing financial strategies. Accurate analysis of how positive or negative news impacts markets provides a significant edge for both individual investors and professional analysts. Leveragai offers AI-driven solutions that leverage cutting-edge generative models to analyze financial news and predict trends.This blog will explain what sentiment analysis is, why it is important in financial decision-making, and how it helps forecast market sentiment. At the end, you'll find a Python-based generative AI application that automates this analysis.Now let's take a closer look together.

What is Sentiment Analysis and Predicting Market Sentiment?

• Sentiment Analysis: Sentiment analysis involves identifying the emotional tone behind textual content, such as news articles, social media posts, and financial reports. Texts are typically categorized as positive, negative, or neutral, and given a score that reflects their emotional stance.

• Predicting Market Sentiment: Market sentiment refers to the general mood or attitude of investors toward a particular market or asset. Positive news can foster optimism, driving prices up, while negative news often leads to caution or market downturns.

Why is Sentiment Analysis Important in Finance?

1. Risk Management: By anticipating potential market fluctuations, investors can manage risks more effectively.

2. Data-Driven Decisions: It enables informed decision-making based on objective data rather than emotions.

3. Competitive Advantage: Gaining early insights into market trends can provide a strategic edge over competitors.

Leveragai’s solutions empower investors to make data-driven financial decisions, combining generative AI with advanced data analytics to deliver timely sentiment insights.

Python Application for Sentiment Analysis

Here, we’ll develop a Python application that uses a generative AI model to analyze financial news sentiment and visualize weekly and monthly trends.This Python application analyzes financial news articles to derive sentiment scores and examines how these scores change over time. It visualizes both weekly and monthly market trends. Below is a step-by-step explanation of the entire code, demonstrating its core components:

Step 1 : Importing Libraries and Defining API Keys

At the start, we load the necessary Python libraries and define API keys for accessing NewsAPI and OpenAI’s GPT-4 models.

import requests  
import openai  
import pandas as pd  
import matplotlib.pyplot as plt  

news_api_key = "YOUR_NEWS_API_KEY"  
openai.api_key = "YOUR_OPENAI_API_KEY"

Step 2 : Fetching News Articles

The application retrieves news articles based on a keyword search from NewsAPI.

def fetch_news(keyword, page_size=50):  
    url = f"https://newsapi.org/v2/everything?q={keyword}&pageSize={page_size}&apiKey={news_api_key}"  
    response = requests.get(url)  
    data = response.json()  
    return data['articles']

Step 3 : Performing Sentiment Analysis

The GPT-4 model evaluates the sentiment of the content and assigns a score between -1 (negative) and 1 (positive).

def analyze_sentiment(content):  
    response = openai.Completion.create(  
        model="gpt-4",  
        prompt=f"Analyze the sentiment of the following financial news content and give a score from -1 (negative) to 1 (positive), with an explanation: '{content}'",  
        max_tokens=100  
    )  
    sentiment_info = response.choices[0].text.strip().split(" ")  
    score = float(sentiment_info[0].split(":")[-1].strip())  
    explanation = sentiment_info[1].split(":")[-1].strip()  
    return score, explanation

Step 4 : Processing News Articles

News content is consolidated into a DataFrame and enriched with sentiment scores and explanations.

def process_news(articles):  
    processed_data = []  
    for article in articles:  
        content = f"{article['title']} {article['description']}"  
        date = article['publishedAt'][:10]  
        sentiment_score, explanation = analyze_sentiment(content)  
        processed_data.append({"Date": date, "Content": content, "Sentiment Score": sentiment_score, "Explanation": explanation})  
    return pd.DataFrame(processed_data)

Step 5 : Calculating Weekly and Monthly Averages

Sentiment scores are grouped by week and month, and averages are calculated.

def calculate_averages(data):  
    data["Date"] = pd.to_datetime(data["Date"])  
    data["Week"] = data["Date"].dt.to_period("W")  
    weekly_avg = data.groupby("Week")["Sentiment Score"].mean().reset_index()  

    data["Month"] = data["Date"].dt.to_period("M")  
    monthly_avg = data.groupby("Month")["Sentiment Score"].mean().reset_index()  

    return weekly_avg, monthly_avg

Step 6 : Visualizing the Sentiment Trends

The application generates plots to visualize weekly and monthly sentiment trends.

def plot_trends(weekly_avg, monthly_avg):  
    plt.figure(figsize=(12, 6))  
    plt.plot(weekly_avg["Week"].astype(str), weekly_avg["Sentiment Score"], label="Weekly Average", marker='o', color='blue')  
    plt.plot(monthly_avg["Month"].astype(str), monthly_avg["Sentiment Score"], label="Monthly Average", marker='o', color='green')  
    plt.xlabel("Time Period")  
    plt.ylabel("Sentiment Score")  
    plt.title("Weekly and Monthly Sentiment Trends")  
    plt.legend()  
    plt.grid(True)  
    plt.xticks(rotation=45)  
    plt.tight_layout()  
    plt.show()

Step 7 : Main Program Flow

All functions are integrated in the main part of the script:

keyword = "Tesla"  
articles = fetch_news(keyword)  
data = process_news(articles)  
weekly_avg, monthly_avg = calculate_averages(data)  
plot_trends(weekly_avg, monthly_avg)

Conclusion

Sentiment analysis is a critical tool to better understand market movements and predict future price fluctuations. With Leveragai's generative AI-based solutions, you can strengthen your investment decisions and analyze market trends in a data-driven way. Visit Leveragai for more and get the best service!