Machine Learning Tool for Email Spam Score Calculator
Hello @JoeMcLaughlin and potential mentors,
This project aims to revolutionize the way we engage and interact with email. Email marketing sending multiple emails as part of a campaign is already enough of a challenge. According to some estimation, about one-fifth of permission-based emails sent by legitimate marketers land in recipients' spam folders. Without using some spam tester tool, emails stand an even greater chance of being marked as spam.
So I'm trying to develop a tool that produces a score of text provided to the model. If the score goes above five on the scale of ten, it will be marked as spam; knowing this beforehand helps a lot to the mass mailer, and then needy textual changes can be made.
I have gone through CiviCRM stack exchange there; I have seen people struggling with low opening rates in the final part of A/B testing and thinking spamminess is one factor. So by having a tool that deals with spam classification would be helpful and handy to the mass mailer.
I believe CRM is known to have a big data hub, so it makes sense to try and utilize those insights for machine learning in creating a tool that helps the user in the domain of email marketing.
Two significant steps involved in building a new open-source email spam score calculator are.
- Experimenting with various spam classification techniques to figure out which one provides a required balance of precision (the fraction of results classified as positive, which are indeed positive) and recall(the fraction of all positive results which were detected).
- Providing an independent web service (like ORES) that can entertain the request to calculate the spamminess(score) of the email.
I am looking forward to know your opinion about this project, and I will soon come up with a detailed proposal that will cover all the algorithmic and implementation parts.
Thanks