Deep Dive: Sentiment Analysis of Tesla News and Investment Recommendation Generation Using DeepSeek-R1

Deep Dive: Sentiment Analysis of Tesla News and Investment Recommendation Generation Using DeepSeek-R1 In stock investing, news sentiment often has a significant impact on stock prices. Real-time monitoring and analysis of news can help us gauge market sentiment and optimize investment decisions. This article uses Tesla (TSLA) as a case study, combining Yahoo Finance, MarkItDown, and the DeepSeek-R1 model to demonstrate a complete workflow from "news acquisition → text extraction → sentiment analysis → investment recommendation." Target Audience: Quantitative engineers, data scientists, fund managers interested in automated financial news analysis, or learners wanting to quickly get started with news sentiment analysis. Feature Overview 1. Fetch Tesla-Related News: Use yfinance to obtain the latest TSLA news. 2. Extract and Clean Text: Parse web content using the MarkItDown library and perform simple regex-based text cleaning. 3. Sentiment Analysis and Recommendations: Score multiple news articles and output investment recommendations based on the DeepSeek-R1 model. Through these steps, you can quickly build a "mini sentiment radar" that aggregates and analyzes multiple news articles. I. Environment Setup 1. Install Python dependencies: 2. Ensure the DeepSeek-R1 model is successfully deployed locally or on a server, accessible via Ollama or similar methods. If you do not have…