Recognizing Emotional Expression in Game Streams

Roohi, Shaghayegh; Mekler, Elisa D.; Tavast, Mikke; Blomqvist, Tatu; Hämäläinen, Perttu

doi:10.1145/3311350.3347197

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…Word and emote embedding vectors are averaged for the chat content model used for input features. For the visual content model, we extract features for each frame using the Visual Geometry Group network(VGGnet) [46], which has been widely used in the recent literature as in [47,48], with 16 layers that were pre-trained on ImageNet and averaged the features for logistic regression input.…”

Section: Feature-based Modelsmentioning

confidence: 99%

Finding epic moments in live content through deep learning on collective decisions

2021

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Live streaming services enable the audience to interact with one another and the streamer over live content. The surging popularity of live streaming platforms has created a competitive environment. To retain existing viewers and attract newcomers, streamers and fans often create a well-condensed summary of the streamed content. However, this process is manual and costly due to the length of online live streaming events. The current study identifies enjoyable moments in user-generated live video content by examining the audiences’ collective evaluation of its epicness. We characterize what features “epic” moments and present a deep learning model to extract them based on analyzing two million user-recommended clips and the associated chat conversations. The evaluation shows that our data-driven approach can identify epic moments from user-generated streamed content that cover various contexts (e.g., victory, funny, awkward, embarrassing). Our user study further demonstrates that the proposed automatic model performs comparably to expert suggestions. We discuss implications of the collective decision-driven extraction in identifying diverse epic moments in a scalable way.

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Section: Feature-based Modelsmentioning

confidence: 99%

Finding epic moments in live content through deep learning on collective decisions

2021

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An automated approach to estimate player experience in game events from psychophysiological data

Siqueira

Fleury

Lamar

et al. 2022

Multimed Tools Appl

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Am I Playing Better Now?

Riahi

Watson

2021

Proc. ACM Comput. Graph. Interact. Tech.

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G-SYNC technology matches formerly regular display refreshes to irregular frame updates, improving frame rates and interactive latency. In a previous study of gaming at the 30Hz frame rates common on consoles, players of Battlefield 4 were unable to discern when G-SYNC was in use, but scored higher with G-SYNC and were affected emotionally. We build on that study with the first examination of G-SYNC's effects at the 60Hz frame rate more common in PC gaming and on emerging consoles. Though G-SYNC's effects are less at 60Hz than they were at 30Hz, G-SYNC can still improve the performance of veteran players, particularly when games are challenging. G-SYNC's effects on emotion and experience were limited.

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Recognizing Emotional Expression in Game Streams

Cited by 4 publications

References 19 publications

Finding epic moments in live content through deep learning on collective decisions

Finding epic moments in live content through deep learning on collective decisions

An automated approach to estimate player experience in game events from psychophysiological data

Am I Playing Better Now?

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