System and method for classifying communications that have low lexical content and/or high contextual content into groups using topics
Disclosed herein are systems, methods, and non-transitory computer-readable storage media for identifying document topics. A system configured to practice the method receives a document from a corpus of documents, learns interpersonal relationships of users associated with the document, performs a lexical analysis of the document, and, based on the interpersonal relationships of the users and the lexical analysis, identifying a topic for the document. The approaches disclosed herein can integrate user-people relationships to identify topics for documents with low lexical or high contextual content. The system can learn this user-people relationship from context. The system uses this learned behavior to identify communication documents correctly. Another aspect is the separation of the two phases. The system overlays the learned model on the lexical topic analysis, allowing the system to capture user-defined topics and user behavior that is learned from other factors such as medium (calls, events, etc) or user preferences.
1. A method comprising:
identifying an interpersonal relationship of a user, the user being associated with a document;
performing a lexical analysis of the document;
based on the lexical analysis and the interpersonal relationship, generating a matrix comprising one of a lexical document-topic relationship matrix, a topic-keyword matrix, or a user-topic matrix, wherein one of the lexical document-topic relationship matrix or the topic-keyword matrix is generated using latent Dirichlet allocation; and
based on the interpersonal relationship, the lexical analysis, and the matrix, identifying a topic for the document.
2. The method of claim 1 , wherein the document has one of low lexical content and high contextual content.
3. The method of claim 1 , wherein the user-topic matrix is generated based on a participant list for each document in a corpus of documents and based on the lexical document-topic relationship matrix and the topic-keywords matrix, wherein the corpus of documents comprises the document.
4. The method of claim 1 , wherein the topic is identified based on a balance of lexical content of the document and the interpersonal relationship.
5. The method of claim 1 , wherein the document is associated with a corpus of documents.
6. A system comprising:
a processor;
a computer-readable storage medium storing instructions which, when executed by the processor, cause the processor to perform operations comprising:
identifying an interpersonal relationship of a user, the user being associated with a document;
performing a lexical analysis of the document;
based on the lexical analysis and the interpersonal relationship, generating a matrix comprising one of a lexical document-topic relationship matrix, a topic-keyword matrix, or a user-topic matrix, wherein one of the lexical document-topic relationship matrix or the topic-keyword matrix is generated using latent Dirichlet allocation; and
classifying the document by topic based on the interpersonal relationship, the lexical analysis, and the matrix.
7. The system of claim 6 , wherein the document has one of low lexical content and high contextual content.
8. The system of claim 6 , wherein the user-topic matrix is generated based on a participant list for each document in a corpus of documents and based on the lexical document-topic relationship matrix and the topic-keywords matrix, wherein the corpus of documents comprises the document.
9. The system of claim 6 , wherein the topic is identified based on a balance of lexical content of the document and the interpersonal relationship.
10. A computer-readable storage device storing instructions which, when executed by a processor, cause the processor to perform operations comprising:
identifying an interpersonal relationship of a user, the user being associated with a document;
performing a lexical analysis of the document;
based on the lexical analysis and the interpersonal relationship, generating a matrix comprising one of a lexical document-topic relationship matrix, a topic-keyword matrix, or a user-topic matrix, wherein one of the lexical document-topic relationship matrix or the topic-keyword matrix is generated using latent Dirichlet allocation; and
based on the interpersonal relationship, the lexical analysis, and the matrix, identifying a topic for the document.
11. The computer-readable storage device of claim 10 , wherein the document has one of low lexical content and high contextual content.
12. The computer-readable storage device of claim 10 , wherein the user-topic matrix is generated based on a participant list for each document in a corpus of documents and based on the lexical document-topic relationship matrix and the topic-keywords matrix, wherein the corpus of documents comprises the document.