Calculate text image ratio in spam mails
Calculating the text-image ratio in spam emails can be a useful technique to identify potential spam emails. Here's a step-by-step guide to calculate the text-image ratio:
Text-Image Ratio (TIR): The text-image ratio is a measure of the proportion of text to images in an email. It's calculated by dividing the number of text characters by the number of images in the email.
Formula:
TIR = (Number of text characters) / (Number of images)
How to calculate TIR:
- Extract the text and images from the email: Use a programming language like Python or a tool like
email-extractor
to extract the text and images from the email. - Count the number of text characters: Count the total number of characters in the text content of the email, excluding any HTML tags or formatting.
- Count the number of images: Count the number of images (e.g., JPEG, PNG, GIF) in the email.
- Calculate the TIR: Divide the number of text characters by the number of images.
Example:
Suppose we have an email with the following characteristics:
- Text content: "Hello, this is a test email."
- Number of text characters: 24
- Number of images: 2 (JPEG and PNG)
TIR = (24) / (2) = 12
In this example, the TIR is 12, which means that for every 1 image, there are approximately 12 text characters.
Interpretation:
A higher TIR value typically indicates that the email contains more text and less images, which is more likely to be a legitimate email. A lower TIR value, on the other hand, may indicate that the email contains more images and less text, which is more likely to be a spam email.
Threshold values:
You can set threshold values for the TIR to classify emails as spam or legitimate. For example:
- TIR < 5: Spam
- TIR ≥ 5: Legitimate
Keep in mind that this is just one of many techniques to identify spam emails, and you may need to combine it with other methods to achieve high accuracy.
Tools and libraries:
If you're interested in implementing this technique in your own project, you can use libraries like:
email
(Python): A built-in Python library for working with email messages.email-extractor
(Python): A library for extracting text and images from email messages.spamassassin
(Perl): A popular spam filtering system that includes a TIR plugin.
Remember to always test and fine-tune your spam filtering system to ensure it's effective and accurate.