  In mobile network, a complaint hotspot problem often affects even thousands
of users' service and leads to significant economic losses and bulk complaints.
In this paper, we propose an approach to predict a customer complaint based on
real-time user signalling data. Through analyzing the network and user sevice
procedure, 30 key data fields related to user experience have been extracted in
XDR data collected from the S1 interface. Furthermore, we augment these basic
features with derived features for user experience evaluation, such as one-hot
features, statistical features and differential features. Considering the
problems of unbalanced data, we use LightGBM as our prediction model. LightGBM
has strong generalization ability and was designed to handle unbalanced data.
Experiments we conducted prove the effectiveness and efficiency of this
proposal. This approach has been deployed for daily routine to locate the hot
complaint problem scope as well as to report affected users and area.
