TakeLab at SemEval-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news

Leon Rotim, Martin Tutek, Jan Šnajder


Abstract
This paper describes our system for fine-grained sentiment scoring of news headlines submitted to SemEval 2017 task 5–subtask 2. Our system uses a feature-light method that consists of a Support Vector Regression (SVR) with various kernels and word vectors as features. Our best-performing submission scored 3rd on the task out of 29 teams and 4th out of 45 submissions with a cosine score of 0.733.
Anthology ID:
S17-2148
Volume:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
866–871
Language:
URL:
https://www.aclweb.org/anthology/S17-2148
DOI:
10.18653/v1/S17-2148
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2148.pdf