Step-by-Step Guide to Sentiment Analysis Automation: US Election & Media

In the vast sea of data and information, discovering valuable insights can seem like searching for a needle in a haystack. But what if you had a powerful tool at your disposal that could sift through the chaff and present you with the needle?

Meet AI-powered sentiment analysis. Here, we’ll guide you step-by-step on how you can leverage the power of AI to analyze news sentiment on the US Election, all without writing a single line of code!

Step 1: Choose Your News Sources via RSS Feeds

RSS feeds are a treasure trove of updates from your preferred news sources. Essentially, these feeds provide a continuous stream of articles from platforms you trust, ensuring that you never miss out on any crucial information.

In our case, we've selected feeds from popular news outlets like CNN, AP, HuffPost, LA Times, NY Times, and Politico. The choice of feeds, however, is flexible and can be adjusted according to your unique needs and interests.

Step 2: Filter the Sources with Make:

The world is awash with data, and not all of it is relevant. To distill this down to useful, targeted insights, we'll use the power of Make. With Make, you can apply filters to the RSS feeds by adding necessary keywords for your specific use case. For this example, we've filtered the news related to the US Election. You'll find that with the right filters, you can effectively separate the wheat from the chaff.

Step 3: Summarize and Clean Content with HuggingFace API

The next step in our automation journey is content summarization. With the help of the HuggingFace API, a powerful NLP tool, we can distill long-form news articles into brief, understandable summaries. This tool connects directly to the Make application, enabling you to receive easy-to-read summaries of the latest news on your Slack channel. This step not only provides you with the essence of each news article but also cleans the content, making it ready for further analysis.

Step 4: Organize and Store Your Data Using Airtable

Data organization is critical for efficient analysis. Using Airtable, a user-friendly no-code database system, you can store and organize all your crucial news data, including the content, URL, and author/channel information. This arrangement allows you to easily access and review the data, which is especially important when dealing with large volumes of information.

Step 5: Conduct Sentiment Analysis

Now comes the exciting part – analyzing the sentiments behind each news article. The goal here is simple yet powerful: classify the sentiment of the article as either Positive or Negative, and identify the subject of the article – in our case, Biden or Trump.

This type of analysis can provide invaluable insights into public opinion and media bias, paving the way for more nuanced understanding and decision-making.

Step 6: Visualize Your Data with Code Interpret

After all the hard work, it's time to enjoy the fruits of your labor. Visualize your newly acquired insights by uploading your table to Code Interpret. The result? A clear, concise stacked bar chart that presents your data in an easily digestible format.

To better illustrate the process and its results, let's look at some examples:

1. An LA Times piece discusses the new GOP primary rules for 2024 that could favor Trump - the sentiment was classified as POSITIVE for Trump.

2. A NY Times article reports on a contest where Trump now faces opposition from everyone else - the sentiment was categorized as NEGATIVE for Trump.

3. A HuffPost story reports on negative events related to Hunter Biden - this was classified as NEGATIVE for Biden.

4. A CNN report discusses the possibility of Trump being on bail by the time of the GOP debate - this was labeled as POSITIVE for Biden.

AI-powered automation opens up limitless possibilities. Imagine having your own personal data center, capable of providing invaluable insights for trend analysis, competitor monitoring, crisis management, or even market research. And the best part? It's free!

By simply adding more sources like social media to your analysis, you can create an even more in-depth view of the news landscape.

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