Title:
|
Towards the understanding of gaming audiences by modeling Twitch emotes
|
Author:
|
Barbieri, Francesco; Espinosa-Anke, Luis; Ballesteros, Miguel; Soler Company, Juan; Saggion, Horacio
|
Abstract:
|
Comunicació presentada al Third Workshop on Noisy User-generated Text (W-NUT 2017), celebrat el dia 7 de setembre de 2017 a Copenhaguen, Dinamarca. |
Abstract:
|
Videogame streaming platforms have become
a paramount example of noisy usergenerated
text. These are websites where
gaming is broadcasted, and allows interaction
with viewers via integrated chatrooms.
Probably the best known platform
of this kind is Twitch, which has more
than 100 million monthly viewers. Despite
these numbers, and unlike other platforms
featuring short messages (e.g. Twitter),
Twitch has not received much attention
from the Natural Language Processing
community. In this paper we aim at
bridging this gap by proposing two important
tasks specific to the Twitch platform,
namely (1) Emote prediction; and
(2) Trolling detection. In our experiments,
we evaluate three models: a BOW baseline,
a logistic supervised classifiers based
on word embeddings, and a bidirectional
long short-term memory recurrent neural
network (LSTM). Our results show that
the LSTM model outperforms the other
two models, where explicit features with
proven effectiveness for similar tasks were
encoded. |
Abstract:
|
Francesco, Luis and Horacio acknowledge support from the TUNER project (TIN2015-65308-C5-5-R, MINECO/FEDER, UE) and the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502). |
Subject(s):
|
-Tractament del llenguatge natural (Informàtica) |
Rights:
|
© 2017 The Association for Computational Linguistics
This material is licensed on a Creative Commons Attribution 4.0 License
http://creativecommons.org/licenses/by/4.0/ |
Document type:
|
Conference Object Article - Published version |
Published by:
|
ACL (Association for Computational Linguistics)
|
Share:
|
|