1,058 research outputs found
Measuring Service Encounter Satisfaction with Customer Service Chatbots using Sentiment Analysis
Chatbots are software-based systems designed to interact with humans using text-based natural language and have attracted considerable interest in online service encounters. In this context, service providers face the challenge of measuring chatbot service encounter satisfaction (CSES), as most approaches are limited to post-interaction surveys that are rarely answered and often biased. Asa result, service providers cannot react quickly to service failures and dissatisfied customers. To address this challenge, we investigate the application of automated sentiment analysis methods as a proxy to measure CSES. Therefore, we first compare different sentiment analysis methods. Second, we investigate the relationship between objectively computed sentiment scores of dialogs and subjectively measured CSES values. Third, we evaluate whether this relationship also exists for utterance sequences throughout the dialog. The paper contributes by proposing and applying an automatic and objective approach to use sentiment scores as a proxy to measure CSES
Designing a Conversation Mining System for Customer Service Chatbots
As chatbots are gaining popularity in customer service, it is critically important for companies to
continuously analyze and improve their chatbots’ performance. However, current analysis approaches
are often limited to the question-answer level or produce highly aggregated metrics (e.g., conversations
per day) instead of leveraging the full potential of the large volume of conversation data to provide
actionable insights for chatbot developers and chatbot managers. To address this challenge, we
developed a novel chatbot analytics approach — conversation mining — based on concepts and methods
from process mining. We instantiated our approach in a conversation mining system that can be used
to visually analyze customer-chatbot conversations at the process level. The results of four focus group
evaluations suggest that conversation mining can help chatbot developers and chatbot managers to
extract useful insights for improving customer service chatbots. Our research contributes to research
and practice with novel design knowledge for conversation mining systems
User Intent Prediction in Information-seeking Conversations
Conversational assistants are being progressively adopted by the general
population. However, they are not capable of handling complicated
information-seeking tasks that involve multiple turns of information exchange.
Due to the limited communication bandwidth in conversational search, it is
important for conversational assistants to accurately detect and predict user
intent in information-seeking conversations. In this paper, we investigate two
aspects of user intent prediction in an information-seeking setting. First, we
extract features based on the content, structural, and sentiment
characteristics of a given utterance, and use classic machine learning methods
to perform user intent prediction. We then conduct an in-depth feature
importance analysis to identify key features in this prediction task. We find
that structural features contribute most to the prediction performance. Given
this finding, we construct neural classifiers to incorporate context
information and achieve better performance without feature engineering. Our
findings can provide insights into the important factors and effective methods
of user intent prediction in information-seeking conversations.Comment: Accepted to CHIIR 201
Affective analysis of customer service calls
This paper presents an affective and acoustic-prosodic analysis of a call-center corpus (700 phone calls with corresponding customer satisfaction levels). Our main goal is to understand how customers’ satisfaction correlates to the acoustic-prosodic and affective information (emotions and personality traits) of the interactions. A subset of 30 calls was manually annotated with emotions (frustrated vs.neutral) and personality traits (Big-Five model). Results on automatic satisfaction prediction from acoustic-prosodic features show a number of very informative linguistic knowledge-based features, especially pitch and energy ranges. The affective analysis also provides encouraging results, relating low/high satisfaction levels with the presence/absence of customer frustration. Concerning personality, customers tend to express signs of anxiety and nervousness, while agents are generally perceived as extroverted and open.info:eu-repo/semantics/publishedVersio
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