Identifying temporal information and tracking sentiment in cancer patients’ interviews

Abstract

Time is an essential component for the analysis of medical data, andthe sentiment beneath the temporal information is intrinsically connected withthe medical reasoning tasks. The present paper introduces the problem of identifyingtemporal information as well as tracking of the sentiments/emotionsaccording to the temporal situations from the interviews of cancer patients.A supervised method has been used to identify the medical events using a list oftemporal words along with various syntactic and semantic features. We alsoanalyzed the sentiments of the patients with respect to the time-bins with thehelp of dependency based sentiment analysis techniques and several Sentimentlexicons. We have achieved the maximum accuracy of 75.38% and 65.06% inidentifying the temporal and sentiment information, respectively.