Week 6 - Reading the News

Hello everybody and welcome back to Week 6 of my Senior Project! This week, my goal was to compile news articles for my Sentiment Analysis model. My objective for this project is to figure out how much the news influences the trend of stock market prices. In order to do this, I have to find news articles that match the increments of my forecasts, which are daily.

At first, I just performed a normal Google search for my first company, Amazon (AMZN). However, when I just searched for “News about Amazon Stock Prices” my results were a mix of various news articles from April 2024 and news outlets that looked like this: 

However, I wanted news articles that were published in the past because in the real world, a person making financial decisions about whether to invest in September 2023 would obviously look at news articles published around that time. Therefore, upon further research, I figured out that I can filter Google Search results based on the date that articles were published. By typing in this input into Google “Amazon (NASDAQ: AMZN) after:2023-09-01 before:2023-09-02” (news articles that mention the stock of amazon published between September 1, 2023 and September 2, 2023), I was able to narrow down my search results to this: 

All these results were published in the time frame that I wanted. For the purposes of this project, I decided to compile data based on the assumption that people would just read the top few articles. Therefore, I decided to take the top three articles from each day and create a dataset with the titles of these articles. If I do this for each day, I’ll have almost 500 articles over the span of the 6 months that I am going to look into per company. News articles can be lengthy, so the model might not be able to handle analyzing 500 full news articles, which is why I chose to use only the titles. I repeated this process for the remaining 7 companies, and created datasets containing the news titles and dates that next week I will spend time preprocessing and getting ready for my Sentiment Analysis model. Thank you so much for reading and I’ll see you next week!