When calls go wrong: how to detect problematic calls based on log-files and emotions?
نویسندگان
چکیده
Traditionally, the prediction of problematic calls in Interactive Voice Response systems in call centers has been based either on dialog state transitions and recognition log data, or on caller emotion. We present a combined model incorporating both types of feature sets that achieved 79.22% classification accuracy of problematic and non-problematic calls after only the first four turns in a human-computer dialogue. We found that using acoustic features to indicate caller emotion did not yield any significant increase of accuracy.
منابع مشابه
Detecting Botnets Through Log Correlation
Botnets, which consist of thousands of compromised machines, can cause significant threats to other systems by launching Distributed Denial of Service (DDoS) attacks, keylogging, and backdoors. In response to these threats, new effective techniques are needed to detect the presence of botnets. In this paper, we have used an interception technique to monitor Windows Application Programming Inter...
متن کاملOnline call quality monitoring for automating agent-based call centers
One of the challenges in automating a call center is the tradeoff between customer satisfaction and the cost of human agents: i.e., most callers prefer human agents to automated systems, but adding human agents substantially increases call center operating costs. One possible compromise is to let callers use automation at the beginning of the call and bring in a human agent if they have problem...
متن کاملDebt Collection Industry: Machine Learning Approach
Businesses are increasingly interested in how big data, artificial intelligence, machine learning, and predictive analytics can be used to increase revenue, lower costs, and improve their business processes. In this paper, we describe how we have developed a data-driven machine learning method to optimize the collection process for a debt collection agency. Precisely speaking, we create a frame...
متن کاملDyVSoR: dynamic malware detection based on extracting patterns from value sets of registers
To control the exponential growth of malware files, security analysts pursue dynamic approaches that automatically identify and analyze malicious software samples. Obfuscation and polymorphism employed by malwares make it difficult for signature-based systems to detect sophisticated malware files. The dynamic analysis or run-time behavior provides a better technique to identify the threat. In t...
متن کاملMining Call Center Conversations exhibiting Similar Affective States
Automatic detection and identifying emotions in large call center calls are essential to spot conversations that require further action. Most often statistical models generated using annotated emotional speech are used to design an emotion detection system. But annotation requires substantial amount of human intervention and cost; and may not be available for call center calls because of the in...
متن کامل