Week 10 - Sentiment Analysis

Hi everybody and welcome to my Senior Project! This week, I continued running sentiment analysis on the datasets that I created to output whether the news article titles were positive or negative or neutral. One dataset in particular that was different from the others was Sharps Technology.

As expected, Sharps Technology had almost no news articles pertaining to it. In fact, throughout the entire 6 months, I could only find less than 10 articles, and there were multiple months where there weren’t any news articles at all pertaining to the company, so I just had to put a “0” for each day. As a result, the sentiment analysis results for Sharps Technologies looks like this:

As compared to the results for a bigger company (Apple) which looks like this: 

The results from the two companies varied drastically. First of all, since there was barely any input data for the sentiment analysis algorithm from Sharps Technologies, it of course returned almost all neutral results. However, since I was able to find at least 3 news articles every single day regarding Apple’s stock, the sentiment analysis algorithm had much more input data and therefore returned a variety of results from positive to negative to neutral. 

I had originally hypothesized that the tone of the news articles might be able to influence the stock price of larger companies. For example, if the media is flooded with negative news about Amazon then people would be less inclined to buy the stock, and maybe even more inclined to sell it causing a decline in the price. However, this wouldn’t be the case for the stock prices of smaller companies, like Sharps Technologies. The further along I progress in this project, the more my hypothesis seems to be supported. There are almost no news articles regarding Sharps Technologies, so the sentiment analysis results are extremely stable, with almost all of them stating “neutral” however from the graphs of the stock prices, we can clearly see that they fluctuate a lot. The sentiment analysis model results definitely won’t make up for the discrepancy between the forecasted and real stock prices of Sharps Technologies. 

The next thing that I have to do is figure out the relationship between the results from my two statistical models. I plan to do this by comparing whether the stock price of a certain company went up/down to whether the tone of the news articles that day were positive/negative. How exactly will I create this program? Stay tuned until next week to find out!