@inproceedings{rotim-etal-2017-takelab,
title = "{T}ake{L}ab at {S}em{E}val-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news",
author = "Rotim, Leon and
Tutek, Martin and
{\v{S}}najder, Jan",
editor = "Bethard, Steven and
Carpuat, Marine and
Apidianaki, Marianna and
Mohammad, Saif M. and
Cer, Daniel and
Jurgens, David",
booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-2148",
doi = "10.18653/v1/S17-2148",
pages = "866--871",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T TakeLab at SemEval-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news
%A Rotim, Leon
%A Tutek, Martin
%A Šnajder, Jan
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F rotim-etal-2017-takelab
%X 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.
%R 10.18653/v1/S17-2148
%U https://aclanthology.org/S17-2148
%U https://doi.org/10.18653/v1/S17-2148
%P 866-871
Markdown (Informal)
[TakeLab at SemEval-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news](https://aclanthology.org/S17-2148) (Rotim et al., SemEval 2017)
ACL