11. A method for managing a message delivery, comprising: receiving a message by a network device; employing the network device to determine a plurality of tokens from the received message based in part on a text body of the received message; employing the network device to analyze the plurality of tokens to assign probability values that the received message is classifiable as one of a plurality of message classes, wherein a probability value is associated with each of the plurality of message classes; employing the network device to select a message class for the received message based on a comparison of the assigned probability values, wherein the assigned probability values represent a plurality of complement probability values for each of the plurality of message classes, and wherein selecting the message class further comprises selecting a message class having a lowest complement probability value; employing the network device to determine a number of tokens in the plurality of tokens that result in a respective token count for each token in a list of tokens for the selected message class exceeding a token frequency threshold based on the selected message class, wherein each token count for each token in the list is selectively decremented over time as a period of time expires for each corresponding token; and based on a comparison between the number of matching tokens in the received message that exceed the token frequency threshold for the selected message class to a matched token threshold provided by a frequency threshold component, employing the network device to identify whether the received message is a spam message or a non-spam message; and employing the message identification as spam or non-spam to at least one of tagging the message, or distributing the message into a message folder.
11. A method for managing a message delivery, comprising: receiving a message by a network device; employing the network device to determine a plurality of tokens from the received message based in part on a text body of the received message; employing the network device to analyze the plurality of tokens to assign probability values that the received message is classifiable as one of a plurality of message classes, wherein a probability value is associated with each of the plurality of message classes; employing the network device to select a message class for the received message based on a comparison of the assigned probability values, wherein the assigned probability values represent a plurality of complement probability values for each of the plurality of message classes, and wherein selecting the message class further comprises selecting a message class having a lowest complement probability value; employing the network device to determine a number of tokens in the plurality of tokens that result in a respective token count for each token in a list of tokens for the selected message class exceeding a token frequency threshold based on the selected message class, wherein each token count for each token in the list is selectively decremented over time as a period of time expires for each corresponding token; and based on a comparison between the number of matching tokens in the received message that exceed the token frequency threshold for the selected message class to a matched token threshold provided by a frequency threshold component, employing the network device to identify whether the received message is a spam message or a non-spam message; and employing the message identification as spam or non-spam to at least one of tagging the message, or distributing the message into a message folder. 15. The method of claim 11 , wherein a statistical classifier that is used to assign probability values is also trained based on messages of known message classes, and wherein the training includes employing at least one of a length normalization for the messages, term frequency transformation, or an inverse message transformation.