Edited by Chima Mmeje

With its vast user-generated content, Reddit is a potential goldmine for SEO Insights. However, manually analyzing this massive amount of data to extract valuable information can be time-consuming and inefficient.

This guide describes how to automate the process using ChatGPT and Notable plugins. We'll navigate the necessary tool setup, data extraction, and analysis to draw actionable insights from Reddit, enhancing your SEO strategy.

By following along, you'll learn to overcome the manual analysis barriers of Reddit, making the data work for you. Also, you'll better understand market trends and consumer interests, allowing for more informed digital marketing decisions.

Many SEO professionals I have encountered are not technically inclined and might be unfamiliar with application programming interfaces (APIs). If you're familiar with APIs, please jump to the "Step-by-step guide to acquiring a Reddit API key" section. Otherwise, let's delve in.

What is an API key?

An API key is similar to a unique password identifying who requests an API. It helps the API service manage access, track usage, and ensure security.

Rate limiting and API quotas

An API rate limit is a control mechanism that defines how many requests a user or application can make to an API within a specified timeframe. This ensures the service remains stable and available to all users by preventing a user or application from overloading it.

Reddit mentions that clients may make up to 60 requests per minute. However, since we will use the Python Reddit API Wrapper (PRAW) in this article, it's important to know about this Python package's rate limits.

Based on PRAW's documentation, the rate limits follow Reddit's rules about how often you can make requests. If you hit the limit, the library waits a bit before making a second attempt. However, if Reddit instructs to wait longer than the set maximum wait time, PRAW stops and shows an error.

Step-by-step guide to acquiring a Reddit API key

If you haven't created a Reddit account yet, do so first. Once registered, log in and proceed with the following steps to navigate to the app preferences page.

Accessing app preferences:

1. Click on your profile icon located at the top-right corner of the Reddit home page.

2. From the dropdown menu, select "User Settings."

3. Click on the "Safety & Privacy" tab on the User Settings page.

4. Scroll down until you see the "App Permissions" section. Click on "Manage Third-Party App Authorization."

The screenshot below provides a visual guide for these steps.

Creating a new application:

1. On the App Preferences page, find and click the button labeled "Are you a developer? Create an app...".

2. You'll see the "create application" form.

• For "name," enter a name for your app.

• In the "App type" dropdown, choose "script."

• Provide a brief "description" for your app. (It is optional but can be filled if desired.)

• In the "redirect uri" field, enter "http://localhost:8080".

3. After filling out the necessary fields, click the "create app" button at the bottom of the form.
4. On the next screen, look for your "client ID" and "secret key."

Having acquired the necessary API key, we will now move on to setting up your Noteable workspace to facilitate smooth interaction with Reddit via ChatGPT. You can review the main chat that generated the results presented in this article using this link.

Installing the Noteable plugin in ChatGPT

Open the menu on the bottom left side of the screen using the 3-dot button. Navigate to the "Settings and Beta" section, select "beta features," and turn on the toggle button for plugins.

Now, go to the plugin mode of ChatGPT, open the plugin store, and search for the Noteable plugin in the dropdown menu. Click the "install" button to navigate to a login page on Noteable's website, where you can either sign up for a new account or log in to your existing account.

After creating an account and logging in, you will be redirected to ChatGPT's page. Activate the Noteable plugin by checking the box next to it in the dropdown menu.

Best practices for securely storing and sharing credentials on Noteable

It's crucial to keep your API keys on Noteable. I highly recommend using this method instead of directly sharing your API keys through ChatGPT. By storing them as environment variables, you can access the keys programmatically without exposing the actual values in the code, making the process more secure.

This step involves creating two secret values required by the PRAW package, which we will name REDDIT_CLIENT_ID and REDDIT_SECRET_KEY. Start by going to the Noteable app’s dashboard. You can see the "Resources" section on the left side menu. Under this section, click on "Secrets." Then, click the "Create a secret" button.

In the "Name" field, enter a name, such as REDDIT_CLIENT_ID, and in the "Value" field, paste your client ID. Set visibility as private, and go through the same process for your secret key, naming it REDDIT_SECRET_KEY and entering the key that we obtained previously. Now, we are ready to move on to the main section of our work, where we will craft a prompt to extract data from Reddit.

Now, the fun part begins. In this section, I will show you how to structure a single prompt to yield your desired output in a streamlined manner. Your goal is to generate an Excel file containing 200 question titles and question bodies from a subreddit.

But first, you must understand the logic behind the prompt

For this task, ChatGPT needs to retrieve the keys that we shared with Noteable (REDDIT_CLIENT_ID and REDDIT_SECRET_KEY), which you can get with the “os” module in Python.

We'll use the PRAW package, a Python wrapper for the Reddit API, to interact with Reddit and extract the required data. PRAW simplifies accessing Reddit's data by handling the connection and retrieval following Reddit's API rules. To create, manipulate, and save the extracted data into a DataFrame and subsequently into an Excel file, we ask ChatGPT to use the Pandas library and its dependencies.

Additionally, the prompt includes guidelines for managing active kernels. A 'kernel' in this context refers to an instance of a notebook's engine that runs the code. Managing kernels is important to ensure efficient use of resources in the Noteable workspace. This is relevant in a Noteable workspace where multiple computations or tasks might run simultaneously. When you reach the limit for active kernels, it's necessary to shut down a kernel to free up resources and ensure the smooth execution of tasks.

You also need to specify whether you want the latest or best-performing posts of the Subreddit. Then, define how you structure the spreadsheet, columns, and the data you extract.

To keep things simple, I suggest using the following columns:

• Question title

• Question body

• Shortlink

• Upvote score

• Number of comments

Example prompt for data extraction

Below is an example of a prompt that encompasses the above points. You can copy this prompt, replace the placeholders with your desired information, and paste it into the ChatGPT interface to initiate the data extraction process:

"Please retrieve {enter a number} of the {newest or hottest} posts from the {subreddit name} subreddit using the PRAW package (ensure PRAW is installed). For each post, extract the question title (name the column 'Question title'), question body (name the column 'Question body'), shortlink, upvote score, and number of comments. Once you've gathered this data, store it in a Pandas DataFrame and reset the index to start from 1 (instead of the default 0). Save the data to an Excel file named '{file-name}.xlsx.' Ensure the Excel file doesn't include an index column. After storing the data, provide a preview of the first three rows.

Import the 'os' module to access environment variables and retrieve the following values from the secrets I shared with Noteable:

REDDIT_CLIENT_ID

REDDIT_SECRET_KEY

You can shut down any kernel when you've reached the limit for active kernels."

This wraps up the data extraction segment of our project. After entering the prompt, ChatGPT will take a couple of minutes to complete the task. Although you can download the Excel file from your Noteable app's dashboard, this is optional to progress.

Here, you will learn how to direct ChatGPT to extract the most repeated 1-word, 2-word, and 3-word queries from the Excel file. This analysis provides insight into the most frequently used words within the analyzed subreddit, helping to uncover prevalent topics. The result will be an Excel sheet with three tabs, one for each query type.

Structuring the prompt: Libraries and resources explained

In this prompt, we will instruct ChatGPT to read an Excel file, manipulate its data, and save the results in another Excel file using the Pandas library. For a more holistic and accurate analysis, combine the "Question Titles" and "Question Text" columns. This amalgamation provides a richer dataset for analysis.

The next step is to break down large chunks of text into individual words or sets of words, a process known as tokenization. The NLTK library can efficiently handle this.

Additionally, to ensure that the tokenization captures only meaningful words and excludes common words or punctuation, the prompt will include instructions to use NLTK tools like RegexpTokenizer and stopwords.

To enhance the filtering process, our prompt instructs ChatGPT to create a list of 50 supplementary stopwords, filtering out colloquial phrases or common expressions that might be prevalent in subreddit discussions but are not included in NLTK's stopwords. Additionally, if you wish to exclude specific words, you can manually create a list and include it in your prompt.

When you’ve cleaned the data, use the Counter class from the collections module to identify the most frequently occurring words or phrases. Save the findings in a new Excel file named "combined-queries.xlsx." This file will feature three distinct sheets: "One Word Queries," "Two Word Queries," and "Three Word Queries," each presenting the queries alongside their mention frequency.

Structuring the prompt ensures efficient data extraction, processing, and analysis, leveraging the most appropriate Python libraries for each phase.

Tested example prompt for data extraction with suggestions for improvement

Below is an example of a prompt that captures the abovementioned points. To utilize this prompt, simply copy and paste it into ChatGPT. It's essential to note that you don't need to adhere strictly to this prompt; feel free to modify it according to your specific needs.

"Let's extract the most repeated 1-word, 2-word, and 3-word queries from the Excel file named '{file-name}.xlsx.' Use Python libraries like Pandas for data manipulation.

Start by reading the Excel file and combining the 'Question Titles' and 'Question Text' columns. Install and use the NLTK library and its necessary resources like Punkt for tokenization, ensuring that punctuation marks and other non-alphanumeric characters are filtered out during this process. Tokenize the combined text to generate one-word, two-word, and three-word queries.

Before we analyze the frequency, filter out common stop words using the NLTK library. In addition to the NLTK stopwords, incorporate an additional stopword list of 50 common auxiliary verbs, contractions, and colloquial phrases. This additional list should focus on phrases like 'I would,' 'I should,' 'I don't,' etc., and be used with the NLTK stopwords.

Once the data is cleaned, use the Counter class from the collections module to determine the most frequent one-word, two-word, and three-word queries.

Save the results in three separate sheets in a new Excel file called 'combined-queries.xlsx.' The sheets should be named 'One Word Queries,' 'Two Word Queries,' and 'Three Word Queries.' Each sheet should list the queries alongside the number of times they were mentioned on Reddit.

Show me the list of the top 5 queries and their count for each group in 3 tables."

Optimizing the number of keywords for faster output

When extracting data from many questions, consider requesting fewer keywords as output to expedite the process. For instance, if you've pulled data from 400 questions, you might ask ChatGPT to show you only the top 3 keywords. If you wish to view more keywords, simply download the file. This approach will reduce ChatGPT's processing time.

Streamlining the prompt for direct output

If you continue to experience interruptions but are not interested in understanding the workflow, consider adding the following line at the end of your prompt: "No need for any explanation; just provide the output." This directive instructs ChatGPT to focus on delivering the desired output.

Data-driven SEO insights with ChatGPT

Now, you have prepared two datasets; the first is a list of questions and their URLs, number of comments, and upvotes. Meanwhile, the second is a list of one-word, two-word, and three-word queries.

To analyze or visualize this data with ChatGPT, use the Noteable plugin or download the Excel files from the Noteable application and upload them to the ChatGPT data analysis tool. For this guide, proceed with the Noteable plugin to maintain consistency within the same chat.

Quick keyword analysis and ideation

Ask ChatGPT to review the data without coding to extract insights from the data. A sample prompt could be:

"Using the Noteable plugin, retrieve the top {number} two-word queries. Then, as an SEO expert using ChatGPT-4, analyze these keywords and provide insights for an SEO professional managing a website focused on the latest {topic} trends."

The screenshot below shows what the outcome might look like:

There are many ways to query ChatGPT for insights. Here are two more tasks you could undertake:

Gap analysis

Compare the keywords in this list with the keywords you're currently targeting on your website or content. This comparison can help identify gaps or opportunities in your current SEO strategy. You can accomplish this task by using a prompt similar to the following:

"Below is a list of the keywords I target for my website. Compare them with the top 30 keywords ranking on each sheet of the file {file-name} and identify gaps or opportunities in my keyword research."

The screenshot below shows how a prompt might look:

ChatGPT will review your keywords, compare them with the extracted data, and provide insights. The following screenshot displays a sample output from this gap analysis task.

Visual exploration of keyword data

Beyond direct analysis, visual analyses of the datasets enhance the understanding of keyword distribution. Below are three creative approaches to explore:

Creating word clouds

Word clouds offer a quick way to view the most prominent keywords within a dataset. This form of visualization can be helpful for an SEO or digital marketer, as it provides quick insight into the terms dominating the conversation.

Below is a sample prompt that you can use in ChatGPT with the Noteable plugin:

"Generate a word cloud using matplotlib and word cloud with dimensions 800 x 600 based on the combined content from both the 'Question Titles' and 'Question Text' columns. If needed, redefine the necessary variables by accessing {enter name of the first excel file here}."

The outcome might look like this:

Generating treemaps

Another engaging way to visualize data is through treemaps. Treemaps provide a visual representation of keyword frequency at a glance. The size of each block within a treemap corresponds to the frequency of the keyword it represents. Hence, larger blocks signify more commonly occurring keywords.

The insight helps SEO and digital marketing professionals identify which keywords garner more attention on Reddit. Below is a sample prompt that you can use in ChatGPT with the Noteable plugin:

"Create a dataset to visualize the top 5 keywords from each of the three keyword groups, excluding the keywords '{1}' and '{2}', resulting in a total of 15 words. The visualization should be a treemap where the size of each block represents the keyword's frequency. Display the plot in the chat interface. While the Squarify library is suggested, consider other suitable visualization libraries if they provide better results."

Below is a sample outcome of this exercise:

Keyword co-occurrence analysis

Keyword co-occurrence analysis is another insightful method to explore. This analysis helps to understand how often specific keywords appear together, showing common topic groupings or associations among keywords.

Here's a sample prompt for use with the Noteable plugin on ChatGPT:

"Using the '{enter name of the first file}.xlsx' dataset, combine the 'Question title' and 'Question body' columns to form a text corpus. Tokenize the text using the NLTK library, filtering out common stopwords, non-alphanumeric characters, and the keywords {1}, {2}, {3}. Calculate the keyword co-occurrence matrix for the top 20 keywords within a window size of 5 words. Visualize the matrix using a heatmap with the seaborn library."

Below is an example of the outcome:

Traditional tools often fall short when extracting keyword insights from vast data pools like Reddit. This guide gives you a simple way to build your own tool with ChatGPT and Noteable without coding skills. Now, you can uncover valuable keywords that resonate with your audience, enhancing your SEO and content strategies to gain a competitive advantage.