SYSTEM AND METHOD FOR SHORT MESSAGE SERVICE (SMS) CONTENT CLASSIFICATION

1 . A method comprising:

receiving, by one or more computing systems, a plurality of messages from a sender, each of the plurality of messages includes metadata and content;

generating, by the one or more computing systems, a sender profile for the sender based on an analysis of the metadata of each of the plurality of messages;

classifying, by the one or more computing systems, each respective message of the plurality of messages as one of a plurality of categories based on a deep learning network analysis of the content of each respective message;

generating, by the one or more computing systems, a sender fingerprint based on a machine learning analysis of the content of each respective message;

determining, by the one or more computing systems, a probability that the sender is a spammer based on the sender profile, the message classifications, and the sender fingerprint; and

tagging, by the one or more computing systems, the sender based on the determined probability.

2 . The method of claim 1 , further comprising:

clustering, by the one or more computing systems, the plurality of messages into a plurality of clusters based on the content; and

determining, by the one or more computing systems, whether one or more of the plurality of clusters is associated with a whitelisted or blacklisted cluster.

3 . The method of claim 2 , wherein clustering the plurality of messages includes:

generating message feature vectors based on the content of each respective message;

generating new message clusters based on the message feature vectors; and

merging the new message clusters with a plurality of existing message clusters.

4 . The method of claim 2 , wherein clustering the plurality of messages includes:

generating message feature vectors based on the content of each respective message; and

employing a spatial partitioning tree using the message feature vectors to generate the plurality of clusters.

5 . The method of claim 1 , wherein classifying the plurality of messages includes:

generating message feature vectors based on the content of each respective message;

employing one or more convolution neural network layers on the message feature vectors;

employing one or more long-short term memory layers on the message feature vectors; and

employing one or more fully connected neural network layers on the message feature vectors to determine a category for each respective message.

6 . The method of claim 1 , wherein generating the sender profile for the sender includes:

aggregating information obtained from the plurality of messages regarding the sender.

7 . A non-transitory computer-readable medium having stored contents that, when executed by one or more computing systems, cause the one or more computing systems to:

receive a plurality of messages from a sender, each of the plurality of messages includes metadata and content;

generate a sender profile for the sender based on an analysis of the metadata of each of the plurality of messages;

classify each respective message of the plurality of messages as one of a plurality of categories based on a deep learning network analysis of the content of each respective message;

generate a sender fingerprint based on a machine learning analysis of the content of each respective message;

determine a probability that the sender is a spammer based on the sender profile, the message classifications, and the sender fingerprint; and

tag the sender based on the determined probability.

8 . The non-transitory computer-readable medium of claim 7 , wherein the stored contents further cause the one or more computing systems to:

cluster the plurality of messages into a plurality of clusters based on the content; and

determine whether one or more of the plurality of clusters is associated with a whitelisted or blacklisted cluster.

9 . The non-transitory computer-readable medium of claim 8 , wherein to cluster the plurality of messages includes:

generate message feature vectors based on the content of each respective message;

generate new message clusters based on the message feature vectors; and

merge the new message clusters with a plurality of previous message clusters.

10 . The non-transitory computer-readable medium of claim 8 , wherein to cluster the plurality of messages includes:

generate message feature vectors based on the content of each respective message; and

employ a spatial partitioning tree using the message feature vectors to generate the plurality of clusters.

11 . The non-transitory computer-readable medium of claim 7 , wherein to classify the plurality of messages includes:

generate message feature vectors based on the content of each respective message;

employ one or more convolution neural network layers on the message feature vectors;

employ one or more long-short term memory layers on the message feature vectors; and

employ one or more fully connected neural network layers on the message feature vectors to determine a category for each respective message.

12 . The non-transitory computer-readable medium of claim 7 , wherein to generate the sender profile for the sender includes:

aggregate information obtained from the plurality of messages regarding the sender.

13 . A system, comprising:

one or more processors; and

at least one non-transitory memory, the non-transitory memory including instructions that, upon execution by at least one of the one or more processors, cause the system to:

receive a plurality of messages from a sender, each of the plurality of messages includes metadata and content;

generate a sender profile for the sender based on an analysis of the metadata of each of the plurality of messages;

classify each respective message of the plurality of messages as one of a plurality of categories based on a deep learning network analysis of the content of each respective message;

generate a sender fingerprint based on a machine learning analysis of the content of each respective message;

determine a probability that the sender is a spammer based on the sender profile, the message classifications, and the sender fingerprint; and

tag the sender based on the determined probability.

14 . The system of claim 13 , wherein the instructions further cause the system to:

cluster the plurality of messages into a plurality of clusters based on the content; and

determine whether one or more of the plurality of clusters is associated with a whitelisted or blacklisted cluster.

15 . The system of claim 14 , wherein to cluster the plurality of messages includes:

generate message feature vectors based on the content of each respective message;

generate new message clusters based on the message feature vectors; and

merge the new message clusters with a plurality of previous message clusters.

16 . The system of claim 14 , wherein to cluster the plurality of messages includes:

generate message feature vectors based on the content of each respective message; and

employ a spatial partitioning tree using the message feature vectors to generate the plurality of clusters.

17 . The system of claim 13 , wherein to classify the plurality of messages includes:

generate message feature vectors based on the content of each respective message;

employ one or more convolution neural network layers on the message feature vectors;

employ one or more long-short term memory layers on the message feature vectors; and

employ one or more fully connected neural network layers on the message feature vectors to determine a category for each respective message.

18 . The system of claim 13 , wherein to generate the sender profile for the sender includes:

aggregate information obtained from the plurality of messages regarding the sender.