TY - GEN
T1 - Tweester at SemEval-2016 task 4
T2 - 10th International Workshop on Semantic Evaluation, SemEval 2016
AU - Palogiannidi, Elisavet
AU - Kolovou, Athanasia
AU - Christopoulou, Fenia
AU - Kokkinos, Filippos
AU - Iosif, Elias
AU - Malandrakis, Nikolaos
AU - Papageorgiou, Harris
AU - Narayanan, Shrikanth
AU - Potamianos, Alexandros
N1 - Funding Information:
Acknowledgements: Elisavet Palogiannidi, Elias Iosif and Alexandros Potamianos were partially funded by the SpeDial project supported by the EU Seventh Framework Programme (FP7), grant number 611396 and the BabyRobot project supported by the EU Horizon 2020 Programme, grant number: 687831.
Publisher Copyright:
© 2016 Association for Computational Linguistics.
PY - 2016
Y1 - 2016
N2 - We describe our submission to SemEval2016 Task 4: Sentiment Analysis in Twitter. The proposed system ranked first for the subtask B. Our system comprises of multiple independent models such as neural networks, semantic-affective models and topic modeling that are combined in a probabilistic way. The novelty of the system is the employment of a topic modeling approach in order to adapt the semantic-affective space for each tweet. In addition, significant enhancements were made in the main system dealing with the data preprocessing and feature extraction including the employment of word embeddings. Each model is used to predict a tweet's sentiment (positive, negative or neutral) and a late fusion scheme is adopted for the final decision.
AB - We describe our submission to SemEval2016 Task 4: Sentiment Analysis in Twitter. The proposed system ranked first for the subtask B. Our system comprises of multiple independent models such as neural networks, semantic-affective models and topic modeling that are combined in a probabilistic way. The novelty of the system is the employment of a topic modeling approach in order to adapt the semantic-affective space for each tweet. In addition, significant enhancements were made in the main system dealing with the data preprocessing and feature extraction including the employment of word embeddings. Each model is used to predict a tweet's sentiment (positive, negative or neutral) and a late fusion scheme is adopted for the final decision.
UR - http://www.scopus.com/inward/record.url?scp=85032379497&partnerID=8YFLogxK
U2 - 10.18653/v1/s16-1023
DO - 10.18653/v1/s16-1023
M3 - Conference contribution
AN - SCOPUS:85032379497
T3 - SemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings
SP - 155
EP - 163
BT - SemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings
PB - Association for Computational Linguistics (ACL)
Y2 - 16 June 2016 through 17 June 2016
ER -