I tried to specify a filter based on the Junk % but could not find 'Junk %' as a criteria.

Am I missing something?

-David

I really like your addons and would gladly provide Czech translation, but if I'm not wrong, there is currently no provision to submit translations. What about registering on BabelZilla for free volunteer translation input? I know this creates development overhead for you, but it would be really nice!

Thanks for your reply,

Karel

If by that you mean the text that you enter into the filter action, that text is just sent directly to javascript. If it is valid in javascript, it should work. If not, then not. You should always be able to add multiple independent search terms with an OR though.

"I have been trying to find in TB source, internet etc the possible values for StatusOfset in Header Regex - which I assume holds values for read, unread, forwarded etc, but can't find any references to what these values are when using this regex"

StatusOfset is "offset in a local mail message of the mozilla status hdr" The misspelled "StatusOfset" is the property name in the mork database. I consider this quite obscure and internal to mailnews code, so I don't know what you are trying to use this. You cannot get status fields from a regex of this. I also don't know why you would want to use a regex to access status fields, since there are more direct ways of using those in search terms.

Thanks for making this really useful add-on. I have a couple of questions...

Is it possible to combine header regex's in one expression? Such as:

(subject:Query|Enquiry)|(sender:smith|jones)

I have tried various combinations but can't get it to work. Just wondered if I was wasting my time.

Also, I have been trying to find in TB source, internet etc the possible values for StatusOfset in Header Regex - which I assume holds values for read, unread, forwarded etc, but can't find any references to what these values are when using this regex. Can you please advise what these values are - or where they are documented.

Thanks!

You're right, that is the action of the "normal" junk column.

I'll just have to remember not to accidently click either column.

Great extension. Thank you.

Alan

Alan said:

Just install JunQuilla and I like it, but:

When I click on the icon in the "junk status plus" column, a good email turns to junk and it disappears into my junk folder.

This can't be what's supposed to be happening, is it?

That is the behavior, if I recall correctly, of the normal junk status column. The intention of the junk status plus column was to replace that column, so junk status plus forwards that click to the main program's code, asking that it do its normal action as if this was the junk status column.

In short, what you described is the expected behavior.

When I click on the icon in the "junk status plus" column, a good email turns to junk and it disappears into my junk folder.

This can't be what's supposed to be happening, is it?

It still seems to me that the addressee field is a poor indicator of junk, but it is how it is and I am always grateful to the guys who are putting these things out in the public domain.

Keep up the good work. :)

What that token is telling you is that, of the messages that you have trained, a much higher proportion of those that were sent to: were junk than good. Even if "a large proportion of the emails that I get (and want)" are like that, an even LARGER portion of those that you get (and don't want) are like that.

Now that could be real, or it could be an issue of inadequate training. For training, you should have about the same number of junk as good messages trained. If it is real though, then the junk analyzer is biased toward marking as "junk", and it should be. Tinkering with that one token is not likely to improve the overall performance of the filter. Look at it this way: a message with no real content, but addressed to you, has a 94% chance of being junk. Think something like image spam here.

On my own system, I am now experimentally using a filter (from FiltaQuilla) to automatically train messages as good that match my address book. I need though to add the capability to limit the number of trained good to be about the same as the number of trained junk, as there can also be issues that arise from too much training of good (particularly if the messages differ in some systematic way from the junk, such as being at different times.)

What that token is telling you is that, of the messages that you have trained, a much higher proportion of those that were sent to: were junk than good. Even if "a large proportion of the emails that I get (and want)" are like that, an even LARGER portion of those that you get (and don't want) are like that.

Now that could be real, or it could be an issue of inadequate training. For training, you should have about the same number of junk as good messages trained. If it is real though, then the junk analyzer is biased toward marking as "junk", and it should be. Tinkering with that one token is not likely to improve the overall performance of the filter. Look at it this way: a message with no real content, but addressed to you, has a 94% chance of being junk. Think something like image spam here.

On my own system, I am now experimentally using a filter (from FiltaQuilla) to automatically train messages as good that match my address book. I need though to add the capability to limit the number of trained good to be about the same as the number of trained junk, as there can also be issues that arise from too much training of good (particularly if the messages differ in some systematic way from the junk, such as being at different times.)

I am puzzled by one issue that can contribute towards false positives.  Looking at the Junk Analysis Detail, the receiving emailing is always allocated a high token value:

to:<myaddress@mydomain.net>          Token 94%

Such a high allocation seems rather odd to me - a large proportion of the emails that I get are addressed to me.  

Polly

The whole point of the Uncertain folder is to force you to train messages that are uncertain, either as good or junk. Once you train a message, it should be removed from the Uncertain folder. The easiest way to do this is with the keyboard, "j" for junk and "shift-j" for good.