8. A method for monitoring conversations at a call center between customers and call center agents in real-time, the method comprising: calculating emotion scores for agents and respective customers in real-time during conversations between the agents and the respective customers; calculating, for each conversation, a first frequency based on the calculated customer emotion scores, wherein calculating the first frequency comprises determining a number of the calculated customer emotions scores that equal or exceed an emotion score threshold during a specified time interval that is less than the entire respective conversation; calculating, for each conversation, a second frequency based on the calculated agent emotion scores, wherein calculating the second frequency comprises determining a number of the calculated agent emotions scores that equal or exceed the emotion score threshold during the specified time interval; detecting, in real-time, specified words or phrases spoken by either the agents or the respective customers during the conversations; displaying, in real-time at an agent station of each agent currently having a conversation with a customer, a visual representation based on the calculated second frequency and a visual representation based on the calculated first frequency; recording conversations between agents and customers; storing the calculated emotion scores for agents and customers; tagging each recorded conversation with event tags, wherein: each event tag represents one of the first frequency, the second frequency, or a generated word/phrase event, and in response to a selection of an event tag, corresponding calculated emotion scores and generated word/phrase events are displayed; and identifying specified emotions and specified additional word/phrase events that were generated during the recorded conversations.
8. A method for monitoring conversations at a call center between customers and call center agents in real-time, the method comprising: calculating emotion scores for agents and respective customers in real-time during conversations between the agents and the respective customers; calculating, for each conversation, a first frequency based on the calculated customer emotion scores, wherein calculating the first frequency comprises determining a number of the calculated customer emotions scores that equal or exceed an emotion score threshold during a specified time interval that is less than the entire respective conversation; calculating, for each conversation, a second frequency based on the calculated agent emotion scores, wherein calculating the second frequency comprises determining a number of the calculated agent emotions scores that equal or exceed the emotion score threshold during the specified time interval; detecting, in real-time, specified words or phrases spoken by either the agents or the respective customers during the conversations; displaying, in real-time at an agent station of each agent currently having a conversation with a customer, a visual representation based on the calculated second frequency and a visual representation based on the calculated first frequency; recording conversations between agents and customers; storing the calculated emotion scores for agents and customers; tagging each recorded conversation with event tags, wherein: each event tag represents one of the first frequency, the second frequency, or a generated word/phrase event, and in response to a selection of an event tag, corresponding calculated emotion scores and generated word/phrase events are displayed; and identifying specified emotions and specified additional word/phrase events that were generated during the recorded conversations. 19. The method of claim 8 , further comprising calculating aggregate emotion score statistics and aggregate word/phrase event statistics for conversations involving a particular agent that occurred during a specified time period.