  A recently introduced classifier, called SS3, has shown to be well suited to
deal with early risk detection (ERD) problems on text streams. It obtained
state-of-the-art performance on early depression and anorexia detection on
Reddit in the CLEF's eRisk open tasks. SS3 was created to deal with ERD
problems naturally since: it supports incremental training and classification
over text streams, and it can visually explain its rationale. However, SS3
processes the input using a bag-of-word model lacking the ability to recognize
important word sequences. This aspect could negatively affect the
classification performance and also reduces the descriptiveness of visual
explanations. In the standard document classification field, it is very common
to use word n-grams to try to overcome some of these limitations.
Unfortunately, when working with text streams, using n-grams is not trivial
since the system must learn and recognize which n-grams are important "on the
fly". This paper introduces t-SS3, an extension of SS3 that allows it to
recognize useful patterns over text streams dynamically. We evaluated our model
in the eRisk 2017 and 2018 tasks on early depression and anorexia detection.
Experimental results suggest that t-SS3 is able to improve both current results
and the richness of visual explanations.
