Systems and methods for identifying issues in electronic documents

A non-transitory computer readable storage medium having instructions stored thereon that, when executed by at least one processor cause the at least one processor to perform operations, the operations comprising:

• retrieving and scoring documents from electronic media sources via a communication network by performing natural language processing on each document of the documents from the electronic media sources and storing full text and a single-valued score from each document in a database;
• identifying at least one issue in each of the retrieved documents according to terms in the retrieved documents including:
• weighing each term in each of the retrieved documents with respect to relevance of the term to each identified issue;
• indicating a distribution for identified issues within each of the retrieved documents;
• formally associating a limited list of specific issues with each retrieved document;
• determining summary metrics for each identified issue by determining a Volume V and a Tone T for each issue based on the number of retrieved documents, and terms in the retrieved documents, respectively, determining an Influencer network strength I for each issue based on the entities identified within the retrieved documents, determining a Speed S for each issue based on a timing of the terms in the retrieved documents, and determining an Emotion E for each issue;
• ranking the issues relative to each other based on weighted summary metrics;
• and
• displaying on a display, the issues ranked in order relative to each other based on the weighted summary metrics.

A method comprising:

• retrieving and scoring documents from electronic media sources via a communication network by performing natural language processing on each document of the documents from the electronic media sources and storing full text and a single-valued score from each document in a database;
• identifying at least one issue in each of the retrieved documents according to terms in the retrieved documents including:
• weighing each term in each of the retrieved documents with respect to a relevance of the term to each identified issue;
• indicating a distribution for identified issues within each of the retrieved documents;
• formally associating a limited list of specific issues with each retrieved document;
• determining summary metrics for each identified issue comprising determining a Volume V and a Tone T for each issue based on the number of retrieved documents, and terms in the retrieved documents, respectively, determining an Influencer network strength I for each issue based on the entities identified within the retrieved documents, determining a Speed S for each issue based on a timing of the terms in the retrieved documents, and determining an Emotion E for each issue;
• ranking the issues relative to each other based on weighted summary metrics;
• and
• displaying on a display, the issues ranked in order relative to each other based on the weighted summary metrics.