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 embedding 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.
Cite
CITATION STYLE
Rotim, L., Tutek, M., & Šnajder, J. (2017). TakeLab at SemEval-2017 Task 5: Linear Aggregation of Word Embeddings for Fine-Grained Sentiment Analysis on Financial News. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (pp. 866–871). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/S17-2148
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