Predicting Satisfaction in Customer Support Chat: Opinion Mining as a Binary Classification Problem
Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
The study explores binary classification with Support Vector Machines as means to predict a satisfaction score based on customer surveys in the customer supportdomain. Standard feature selection methods and their impact on results are evaluated and a feature scoring metric Log Odds Ratio is implemented for addressingasymmetrical class distributions. Results show that the feature selection andscoring methods implemented improve performance significantly. Results alsoshow that it is possible to get decent predictive values on test data based onlimited amount of training observations. However mixed results are presentedin a real-world application example as a there is a significant error rate fordiscriminating the minority class. We also show the negative effects of usingcommon metrics such as accuracy and f-measure for optimizing models whendealing with high-skew data in a classification context.
Place, publisher, year, edition, pages
2016. , 34 p.
Binary Classification, Customer Support, Chat, Opinion Mining, Support Vector Machines
Other Engineering and Technologies not elsewhere specified
IdentifiersURN: urn:nbn:se:uu:diva-300165OAI: oai:DiVA.org:uu-300165DiVA: diva2:950933
Bachelor Programme in Language Technology