By the end of this video, you will find out whether Elon Musk writes positive, negative, or neutral tweets on his social media platform, Twitter X, and how this has been evolving over time. You will also learn how to perform advanced sentiment analysis for any social media platform with no coding experience required.
To get started, go to GPT4 and navigate to the plugins section. Install the Notable plugin, which allows you to run Python directly inside your GPT and analyze big data sets as well as visualize data directly in your chat. If you don’t have the Notable plugin installed, you can find it in the plugin store and install it.
Once you have installed the Notable plugin, create a free account on notable.io and log in. Create a project and upload your data set, which should include the date and time of the tweet, the specific tweet text, and the username. The data set should cover a significant period of time, such as Elon Musk’s tweets from January 2012 to June 2023.
After uploading your data set, copy the URL of the data set in the Notable app. This URL will be used by the Notable plugin to access the data set. Next, open the GPT prompt and paste the prompt code for sentiment analysis using the Vader sentiment analysis tool. This tool is pre-trained to analyze sentiments in social media text and can categorize tweets as positive, neutral, or negative.
Once you have set up the prompt, send it to GPT. The Notable plugin will be used to load the data set and create a Jupiter notebook for analysis. The notebook will install the Vader sentiment analysis tool and compute the sentiment scores for each tweet. It will also generate visualizations of the sentiment trends over time.
By analyzing the visualizations, you can observe trends in Elon Musk’s tweets. Before 2018, Musk tweeted less frequently, and most of the tweets were positive with some negative ones. However, after 2018, Musk started tweeting more frequently, and the number of negative tweets increased. The sentiment analysis also confirms that the majority of Musk’s tweets are positive, followed by neutral and negative tweets.
In conclusion, analyzing Elon Musk’s tweets using advanced sentiment analysis techniques can provide insights into his social media presence and how it has evolved over time. The Notable plugin and the Vader sentiment analysis tool make it easy to perform such analysis without any coding experience. If you’re interested in learning more about notable plugins and data analysis, check out the playlist mentioned in the video.
Thank you for watching and have a great day!
