engleski

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

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.

sentiment analysis ; kernel regression ; support vector machine

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