FeelsGoodMan: Inferring Semantics of Twitch Neologisms
This addresses the challenge of real-time language understanding for Twitch users and platforms, though it is incremental as it builds on existing methods.
The paper tackled the problem of understanding neologisms in Twitch chats, specifically emotes, by establishing a new baseline for sentiment analysis that outperformed the previous supervised benchmark by 7.9% points and introducing an unsupervised framework to auto-generate a pseudo-dictionary of emotes.
Twitch chats pose a unique problem in natural language understanding due to a large presence of neologisms, specifically emotes. There are a total of 8.06 million emotes, over 400k of which were used in the week studied. There is virtually no information on the meaning or sentiment of emotes, and with a constant influx of new emotes and drift in their frequencies, it becomes impossible to maintain an updated manually-labeled dataset. Our paper makes a two fold contribution. First we establish a new baseline for sentiment analysis on Twitch data, outperforming the previous supervised benchmark by 7.9% points. Secondly, we introduce a simple but powerful unsupervised framework based on word embeddings and k-NN to enrich existing models with out-of-vocabulary knowledge. This framework allows us to auto-generate a pseudo-dictionary of emotes and we show that we can nearly match the supervised benchmark above even when injecting such emote knowledge into sentiment classifiers trained on extraneous datasets such as movie reviews or Twitter.