  Context. The game industry is increasingly growing in recent years. Every
day, millions of people play video games, not only as a hobby, but also for
professional competitions (e.g., e-sports or speed-running) or for making
business by entertaining others (e.g., streamers). The latter daily produce a
large amount of gameplay videos in which they also comment live what they
experience. But no software and, thus, no video game is perfect: Streamers may
encounter several problems (such as bugs, glitches, or performance issues)
while they play. Also, it is unlikely that they explicitly report such issues
to developers. The identified problems may negatively impact the user's gaming
experience and, in turn, can harm the reputation of the game and of the
producer. Objective. In this paper, we propose and empirically evaluate GELID,
an approach for automatically extracting relevant information from gameplay
videos by (i) identifying video segments in which streamers experienced
anomalies; (ii) categorizing them based on their type (e.g., logic or
presentation); clustering them based on (iii) the context in which appear
(e.g., level or game area) and (iv) on the specific issue type (e.g., game
crashes). Method. We manually defined a training set for step 2 of GELID
(categorization) and a test set for validating in isolation the four components
of GELID. In total, we manually segmented, labeled, and clustered 170 videos
related to 3 video games, defining a dataset containing 604 segments. Results.
While in steps 1 (segmentation) and 4 (specific issue clustering) GELID
achieves satisfactory results, it shows limitations on step 3 (game context
clustering) and, above all, step 2 (categorization).
